Workshop by Ignacio Martinez (Oracle) on agent memory and harness engineering. Core thesis: a language model's weights are frozen and rented, so everything controllable lives in the surrounding harness — the work of turning a nondeterministic core into repeatable output. Topics include the seven layers of a harness; where memory physically lives (files vs. databases, and using work trees for parallel agents); agent memory and the data sprawl RAG pipelines create; context rot and quadratic attention; a semantic layer drawing on Jakob von Uexküll's concept of Umwelt as an agent's perceptual lens; the toolbox pattern for context engineering; skill/workflow promotion; and a hysteresis variable encoding how much patience the harness extends to the model. Includes hands-on workshop setup with a repo of to-dos starting from a bare model call. # Channel Presentation Observations — "Total Recall: Agent Memory and Harness Engineering — Ignacio Martinez, Oracle" ## Video Format - **In-person technical workshop** (~2 hours: ~30 min intro, ~90 min hands-on), delivered live at what appears to be an AI engineering conference (speaker references "the ai.engineer Wi-Fi" and an "Oracle booth"). - Includes a hands-on lab: participants register via a website ("workshopwaitingroom.com"), receive a GitHub repo invitation, and run code in **GitHub Codespaces**. - Format includes live audience Q&A and interactive elements (raise of hands, colleagues circulating the room). ## Intended Audience - Explicitly addresses **AI engineers / AI developers**: *"raise of hands, who is working as an AI engineer or as an AI developer? Oh my god, okay."* - Audience is assumed hands-on and technical — the workshop has *"a total of 19 things that you need to do"* in a student notebook, with Python/VS Code familiarity assumed. - Enterprise/developer-oriented framing (mentions enterprise customers, data security, data retention concerns). ## Claimed Scope - Core topics: **agent memory and agent harness engineering** — *"some knowledge on agent memory and agent harnesses, how you can build your own agent harness."* - Technical coverage demonstrated: agent stack layers, files-vs-databases memory debate, ACID/transactional consistency, work trees, vector search/HNSW, bi-encoders/rerankers, in-database embeddings, context rot, short/long/shared memory, the "Umwelt" semantic layer, agent loops, toolbox/skillbox patterns, continual learning, skill promotion/distillation. ## Explicit Creator Self-Description - Speaker: **Ignacio Martinez**, *"I've been working for Oracle for 7 years. I've been a developer advocate for about four of them."* - Credibility claim: *"I launched a course with Andrew Ng on agent memory"* and is *"in the process… [of recording] a new course with Andrew Ng on continual learning for AI agents."* - Active on GitHub; references talks and profile. ## Observable Presentation Patterns - **Strong vendor alignment**: repeatedly positions Oracle products — Oracle DBFS, LangChain OracleDB integration, Oracle Agent Memory Package (OAMP), OCI Generative AI service (*"think of us as the enterprise open router"*), and the converged database (*"the only converged database in the market"*). - **Promotional call to action**: *"what I want you to do today is just to try us out as Oracle. Try our database."* - **Educational-but-branded tone**: balances neutral conceptual teaching (agent loop, context rot, memory taxonomy) with Oracle-specific solutions. - Community-building touches: Discord server for the Oracle Agent Memory Package, invitations to the Oracle booth, offering slides via LinkedIn, self-funded Codespaces (*"I'm paying for this myself"*). - Pedagogical style: conversational, humor-inflected (*"If you finish first… I will give you, because I don't have anything else"*), interactive, tolerant of live troubleshooting (Wi-Fi issues, demo restart). ## Claims vs. Demonstrated - **Creator claim**: Oracle is "the only converged database in the market" — a vendor assertion, not independently verified in the material. - **Demonstrated**: working demo app ("Total Recall" app book) showing tool selection, agent traces, context-window visualization, fault-tolerant agent loop behavior — observable in the live demo portion. # Delivery Observation: Ignacio Martinez — "Total Recall: Agent Memory and Harness Engineering" ## Overall Delivery Style - **Conversational and informal** for a technical workshop. Opens with *"Hello, hello. Perfect. Yep."* and repeatedly uses casual fillers: *"you know," "right?" "kind of," "like," "um."* - **Audience-aware humor and warmth**: acknowledges the early slot — *"thank you, by the way, for being here because I know it's Monday 9:00 a.m."* and later *"If you finish first, raise your hand, and I will give you a hug, because I don't have anything else."* ## Tone - **Enthusiastic, self-deprecating, playful.** Self-referential jokes appear multiple times: - *"If you're a degenerate like me, and you are working, I don't know, like eight, 16, 32 agents at a time."* - *"If you're like me, you're subscribed to every imaginable subscription on Earth."* - *"I assume no one is a millionaire or not many of us are millionaires."* - **Humor under stress**: during demo failures — *"Demo time," "Okay, not fun,"* and when reconnecting, *"I feel like Caesar."* - Occasionally edgy/casual: *"you want to punch me, right? Because I haven't shut up"* (context rot analogy), *"very caveman style"* (named entity recognition). ## Recurring Phrases and Verbal Tics - **"Right?"** as a constant comprehension check: *"they are the frozen part of the reasoning, right?", "It's the part that we rent... that we do not control. Right?"* — appears dozens of times. - **"You know"** as filler: *"And, you know, my talks and I'm very active on GitHub," "you know, lots of other lots of other things."* - **"Kind of"** as a hedge: *"kind of think of of a large language model like a isolated thing," "it's kind of this observing and reasoning and then acting part."* - **"Frozen"** as a key term, repeated deliberately: *"the frozen part of the reasoning," "the frozen reasoning core."* - **"Reliable and repeatable/predictable outcomes"** — repeated as the harness goal: *"create reliable and predictable outputs over and over," "produce reliable and repeatable results."* ## Teaching Habits - **Analogy-driven explanation** of abstract concepts: - Context rot via attention in conversation: *"if the conversation goes on for 8 hours, then you want to punch me."* - Umwelt via human senses: *"For us humans... we have our eyes, our senses... an agent doesn't have human like senses."* - Oracle as *"the enterprise open router."* - **Concrete framing before abstraction**: defines an agent simply — *"a model large language model plus a harness"* — then unpacks it. - **Interactive checks**: *"raise of hands, who is working as an AI engineer?"* and *"Any suggestions? Work trees, exactly."* - **Logistics transparency**: states the time split upfront — *"first 30 minutes... introduction... then in the next hour and a half we're going to go... through the workshop together."* - **Invites questions explicitly**: *"if you have any questions, just raise your hand. I'm very happy to take questions."* ## Communication Patterns - **Sales-adjacent moments woven into teaching**, softened with disclaimers: *"I will not name names, so I don't get in trouble"* and *"just try us out as Oracle... We are called the converged database."* Also, *"just think of us as the enterprise open router."* - **Acknowledges colleagues and credits people by name**: *"Valentin here, he's working on the development of this agent memory package,"* plus promotion of courses with Andrew Ng. - **Careful self-correction mid-speech**: *"they will merge to master, or the merge to main, sorry"*; frequent restarts of sentences ("we're going to we're going," "the on the on the"). ## Uncertainty / Isolated Moments - The humor under demo failure (*"Demo time," "not fun," "I feel like Caesar"*) appears only in one segment; treat it as a situational response rather than a stable pattern. - The self-deprecating "like me" jokes recur three times — likely a stable habit, but drawn from a single session, so generalization is limited. - Audience Q&A responses show a consistent **"great question"** acknowledgment pattern (*"Great question. So his question was..."*), appearing twice — suggestive but thin evidence. # Observation Record: "Total Recall: Agent Memory and Harness Engineering" — Ignacio Martinez, Oracle ## Session Overview - **Speaker**: Ignacio Martinez, self-described Oracle employee of 7 years, developer advocate for about 4 of them. He states he launched a course with Andrew Ng on agent memory, and mentions (with "Cassius," an audience member) an upcoming course with Andrew Ng on continual learning for AI agents. - **Format**: Workshop session, roughly 30 minutes of introduction followed by ~1.5 hours of hands-on workshop run in GitHub Codespaces, using a repo accessible via workshopwaitingroom.com registration. - **Workshop logistics stated by speaker**: 19 "to-dos" in a student notebook; attendees build an agent harness layer by layer starting from a bare model. He notes he is personally paying for Codespaces resources ("please don't [use 16 cores] because I'm paying for this myself"). ## Core Positions and Claims ### The Agent Stack Martinez describes five layers every AI agent sits on: - **Application** (product surface), **Data** (memory, knowledge, retrieval, encoding, search), **Model** (reasoning LLM), **Infrastructure** (orchestration, model serving), **Compute** (cloud/GPUs, database engine). - **Claim**: All layers except data are "increasingly commoditized"; therefore the data layer is where engineers have the most control, and it is where the agent harness "lives." ### Definition of an Agent and Harness Engineering - **Claim (his definition)**: "An AI agent is essentially a model plus a harness" — "an autonomous entity whose cognitive functions are powered by a large language model for reasoning... augmented by a database or files for memory, extended through tools for actions and grounded in inputs that let it perceive its environment." - **Claim**: Models are the "frozen" reasoning core — weights typically don't change unless you have "millions of dollars or... GPUs." - **Goal of harness engineering**: produce "reliable and predictable outputs over and over," converting the non-deterministic nature of LLMs into repeatable results. - **Claim**: Models are swappable via common interfaces (OpenAI protocol, Anthropic API spec), so the harness is the differentiating layer. - **Claim**: Modern AI systems (Claude Code, Codex) combine **automation** (reliability) and **autonomy** (flexibility). ### Shapes of AI Applications Martinez identifies four: **LLM chatbots** (passive), **RAG applications** (semi-passive), **LLM-driven workflows** (automation), and **AI agents** (autonomy). ### Seven Layers of an Agent Harness He enumerates (not all fully listed in transcript, but covered components include): - **Storage layer** — where memory physically lives. - **Memory engineering components** — encoding, search, retrieval. - **Semantic layer** — the "hidden vocabulary"/tribal knowledge assumed by the LLM. - **Agent loop** — the driver making a model an agent; observe–reason–act; must be "failure resistant so that we never exit the loop." - **Context engineering** — keeping the context window "as salient as possible," minimizing it so the task stays relevant. ### Files vs. Databases Dilemma - **Claim**: Since roughly January, a debate exists between file maximalists and database maximalists; "both things are right." - **Files advantages (his account)**: match model instincts, easy to create/append, unstructured, POSIX semantics, OS-compatible. - **Files disadvantages**: no transactional consistency (citing a workshop audience member's suggestion of **git work trees** as the current workaround — agents work in separate work trees then merge to main), no hybrid search, no backups. - **Databases advantages**: ACID consistency, high availability, replication, vector search — "things that the database fixed 35–40 years ago, and people have kind of forgotten about that." - **Claim**: A hybrid approach is best — e.g., short-term memory in files; promoted long-term memory (user preferences) in a database. - **Oracle positioning**: Introduces **DBFS** (database file system) — files stored inside the database, giving files ACID consistency, vector search, relations, security, high availability. ### Converged Database Argument - **Claim**: AI engineers face "data synchronization logic overhead" when different data types (text, JSON metadata, vectors) require different databases (he declines to name vendors: "I will not name names, so I don't get in trouble"). - **Oracle claim**: The "only converged database in the market" supporting JSON, relational, spatial, graph — one engine, one query interface, one development stack; a "single attack vector" to secure. ### Memory Taxonomy - **Agent memory defined**: mechanisms and systems allowing an agent to "retain, reuse, refine, and recall information." - Components: **short-term** (ephemeral, e.g., coding agent to-do list), **long-term** (**episodic** — past conversations, e.g., refining workflows from Claude history; **procedural** — successful workflows turned into reusable repeatable processes), and **shared memory** ("relatively new") — parent/sub-agent communication or multi-agent collaboration. ### Against "Just Use a Huge Context Window" - **Claim**: Context window is itself a form of short-term memory, "useful for some things, but not for all of them." - **Context rot / degradation**: the more items in the context window, the less attention per item; the attention matrix "scales quadratically" (one token referenced against every other token), so keep the context window small. - He uses an analogy: a 30-minute conversation holds attention; after 8 hours "you want to punch me... you haven't learned almost anything." ### Oracle Agent Memory Package (OAMP) - **Claim**: Without a managed solution, engineers must decide when to compact, summarize, what to keep/extract, token budgets; OAMP does these "in one single line of code," reducing cognitive load. - **Context card structure**: topics (orient the model), summary (compacts thread, states current intent), relevant information (facts, preferences, memories), episodic memories (track unanswered questions), recent messages (local context). ### Audience Exchange on OAMP (Q&A) - **Audience member**: "So, you're not suggesting that this goes directly into the model. This is actually something that is used by the furnace [harness]... to harness what's going into the model." - **Martinez**: Confirms ("Exactly. Yes. Yes.") — the structure is "an abstraction that we build on top of the model because the model we have no control over in most cases." ### Semantic Layer / Umwelt - References **Jakob von Uexküll's** concept of *Umwelt*: every organism perceives reality through a lens determined by what it has access to. - **Claim**: Agents lack human senses but have a semantic lens shaped by training data and provided context; the semantic layer is "the agent's Umwelt." - **"The Unsaid"**: organizational/institutional tribal knowledge — how data is modeled, how queries execute, metadata — things colleagues don't state explicitly but a newcomer (or an agent) must be given. ### Continual Learning - Three types of continual learning identified: in the **weights**, in the **representation** (embeddings, re-ranking), and in the **context window**. - **Claim**: The workshop focuses on context/token space because it is "the easiest" and "least expensive" — "I assume no one is a millionaire." - **Skill/workflow promotion**: successful multi-hour workflows are stored in memory; promoted skills can go through a **distillation process** producing a better `skill.md`, retiring the old version — continual learning "on our own skills," customized to tone, preferences, preferred libraries/databases. ### Toolbox / Skillbox Pattern and Q&A - **Claim**: Tools and skills should be stored for retrieval and loaded into context only when needed, re-evaluated each agent-loop iteration. - **Q&A on thousands of tools**: Martinez cites **HNSW (hierarchical navigable small world) indexes** — each graph node a vector index — making tool retrieval negligible in cost, available "only in the data, not in files." He adds common tool calls (read file, write file, grep) "typically don't exceed 100 or 1,000." - **Q&A on similar/confidential tool descriptions across business units**: he proposes **LLM-enhanced tool descriptions** to increase vector separability, as opposed to "caveman style" named entity recognition. ### Agent Loop Limits and Model Routing - **Q&A on retry limits**: He confirms a cutoff exists — "we don't have infinite money" — describing a "hysteresis variable that holds the amount of patience that the harness will get with the model." For the model used (referred to as "4.1 fast reasoning"), he found **8–12 maximum tool calls** before giving up appropriate, depending on model accuracy. - **Claim (his personal opinion)**: "The future is mixture of small experts" — ~100M-parameter models excellent at one task, with an aggregator/orchestrator routing queries; this yields a "very token efficient" harness. He notes some companies charge a percentage of token savings for routing services. - **Demo detail**: A sample query took 16 steps in one run, 2 in another; the harness detected an error and continued due to fault tolerance. ### Oracle Inference Positioning - **Claim**: Oracle's OCI Generative AI service has partnerships with **Google, Meta, OpenAI, and xAI** "for the time being," providing inference through a managed service — "think of us as the enterprise Open Router, if you'd like." ## Speaker Qualifications and Asides - Notes he is "very maximalist" about neither files nor databases ("I will not include myself"). - Weights "typically" don't change — qualified because of his in-progress continual-learning course work. - Audience hand-raise showed most attendees work as AI engineers/developers. - Invites attendees to the Oracle booth and the OAMP Discord server, and mentions colleagues ("Valentin," who worked on the agent memory package) are present in the room. ## Unresolved / Tension Points - The **files vs. databases** debate is presented as ongoing with both sides "right"; his resolution is a hybrid, asserted rather than demonstrated in the lecture portion (the three-agent counter race demo is deferred to the workshop, where he says "I know the answer, of course, but you'll get to know it later"). - His agent definition ("model plus harness") is explicitly framed as his own: "to me, an AI agent is essentially..." - Model-routing economics and small-experts future are framed as personal opinion, not settled fact. # Video Attribution Record ## Video Metadata - **Title:** Total Recall: Agent Memory and Harness Engineering — Ignacio Martinez, Oracle - **Video ID:** `01a15ca7-5bfd-7f7d-92a6-e1751d674552` - **Format:** Live workshop session (presentation + hands-on lab), Monday 9:00 a.m. slot ## Speakers and Roles ### Primary Speaker — **Ignacio Martinez** (Creator / Workshop Instructor) - **Role:** Lead presenter, workshop instructor, Oracle Developer Advocate - Self-described credentials (stated in transcript): 7 years at Oracle, ~4 years as developer advocate - Claims to have **launched a course with Andrew Ng on agent memory**; announces an upcoming course on **continual learning for AI agents** with Andrew Ng and a collaborator named **Cassius** (present in the room) ### Audience Members / Attendees (Unidentified) - Multiple unnamed audience questioners (transcribed as `>>` interjections); questions on: - Harness vs. direct model injection of context - Handling thousands of tools (toolbox pattern / HNSW indexes) - Similar tool descriptions across business units / confidential data access - Agent loop retry limits / hallucination cutoffs - No audience member is named or given a persistent role ### Named Third Parties (Present but Not Speaking in Transcript) - **Valentin** — Oracle colleague, identified as developer of the agent memory package; available for questions - **Cassius** — collaborator on upcoming continual learning course - Other unnamed Oracle colleagues assisting around the room (Wi-Fi help, code space setup) ## Referenced Sources and Third-Party Claims ### Oracle Products and Services (Speaker's Own Organization) - **Oracle DBFS** (database file system) - **OAMP — Oracle Agent Memory Package** (context cards, memory management) - **OCI Generative AI service** — claimed partnerships with **Google, Meta, OpenAI, xAI** for managed inference - **LangChain OracleDB** integration - **In-database embeddings** (claimed benefit for data security/retention) - **Oracle "converged database"** — speaker claims Oracle is "the only converged database in the market" ### External Technologies Cited - GitHub Code Spaces (workshop delivery platform) - OpenAI completions API, Anthropic API specification (interoperability interfaces) - Claude (memory features reference) - HNSW (hierarchical navigable small world) indexes - POShttps://example.com — POSIX semantics, git work trees - Concept attribution: **Umwelt**, coined by **Jakob von Uexküll** ### Referenced Course - Course with **Andrew Ng** on agent memory (existing) and on continual learning for AI agents (upcoming); described as "this course with Andrew Young" in one audience-answer passage (likely a transcript rendering of "Andrew Ng") ## Promotional Segments (Flagged) > "We are the only converged database in the market… We can be the engine of your AI applications." > "Think of us as the enterprise open router, if you'd like." - Repeated promotion of Oracle database, DBFS, OAMP, and OCI Generative AI as solutions to problems raised in the workshop - Promotion of the Oracle **Discord server** for OAMP users - Invitation to visit the **Oracle booth** at the event - Registration site **workshopwaitingroom.com** (speaker states he registered the domain himself for the workshop; codespaces reportedly paid personally by the speaker) - Speaker note: workshop repo on GitHub — he thanks attendees for "starring" it ## Availability and Provenance Limits - **No curated metadata supplied** — no comment availability, publication date, channel, or verified speaker identity beyond transcript self-identification - Slides offered informally via Discord/LinkedIn on request; not otherwise available - Session content partially depends on a live demo; a demo failure (codespace disconnect) occurs and is recovered on-screen - Third-party claims (partnerships, market exclusivity) are **speaker assertions only**; no independent verification in source Hello, hello. Perfect. Yep. So, perfect. Yeah, this this volume is perfect. It's just a website that I registered the domain on Saturday just to do the registration of the workshop. But, for those of you who are going to follow along with me, just know that after you complete that that process in the in the workshop, you will get an invitation to the repository. And just view the invitation, accept it, and then you'll be able to run the workshop. We'll be running the workshop on GitHub code spaces. So, if you want to start that up, it takes like 5 minutes. Um just for reference, I'm going to use the first 30 minutes to give you an introduction to Agent Harness and lots of other concepts, and then in the next hour and a half we're going to go actually go through uh the workshop together. Sound good? Okay, perfect. And thank you, by the way, for being here because I know it's Monday 9:00 a.m. So, I commend you all for for being here. \>\> Uh just 2 minutes before we begin, so I will shut up. \>\> All right. Well, let's let's begin, guys. So, thank you for being on this uh at this time here with me. Um I know it's 9:00 a.m. like I said, so hopefully I can and and we me and my team can make your time worthwhile. At the end of this session, what I would like you to leave with is some knowledge on agent memory and agent harnesses, how you can build your own agent harness, which is nowadays one of the hot topics on AI, I would say. Um Lots of people are talking about models constantly, but the thing is that models, like language models, they are the frozen part of the reasoning, right? We just have to accept what we are given. And during the past few weeks, you will if you're following the news, you will have seen that this is this has never been more true than now. So, the harness is what we're going to talk about. For those of you who weren't here, uh you can scan this QR code or go to that website workshopwaitingroom.com and just register. You will get an invitation to a GitHub repo. And on this GitHub, you will be able to create a GitHub code space where we will run the workshop. And you'll have everything set up for you. So, just a little introduction on on who I am. I've been working for Oracle for 7 years. I've been a developer advocate for about four of them. And you can find, you know, my talks and I'm very active on GitHub as well. So, if you're a GitHub user, just check out my GitHub profile if you like. Uh what is the highlight of my career so far? I launched a course with Andrew Ng on agent memory. So, if you're interested on the memory components of what we're going to what we are going to discuss today, you can just check out that course if you'd like. Um these are the things that we are going to talk through today, right? First of all, we're going to do a little introduction on what the agent stack is, and then we're going to zoom into the data layer where the memory lives. Then we're going to explore a little bit about the shapes, the different shapes that AI applications take nowadays, uh what an agent is uh followed by the seven different layers that make up an agent harness. So, we if you just follow these seven different structures, you will be able to create an agent harness, a minimal agent harness that you can connect any model to. Then, we're going to finally talk a little bit about continual learning and how as as Oracle we are uniquely positioned to help you achieve and develop agent harnesses and and AI applications. So, the agent stack. And by the way, \[clears throat\] if you have any questions, just raise your hand. I'm very happy to to take questions, as well. So, the agent stack. The agent stack, like every agent a every AI agent, sits on on these five layers. You either you have an application, which is the product surface, right? What we interact with as users. We have data. And that has lots of components. You have memory, you have knowledge, you have retrieval, encoding, search. You have the model itself, the reasoning large language model that is behind everything. Uh infrastructure, which is the orchestration. What model do we serve depending on the reasoning effort that we need and things like that. And then we have compute, which is the cloud or GPUs and also the the database engine. And the application, the model, the infrastructure, and the compute, these four layers except for the data, they are uh increasing increasingly commoditized. What do I mean by that? They are trying to take away the complexity from these layers out of out of our domain, right? So, the thing that we have the most control over when working with AI applications is actually the data. And that's the part that we're going going in because the agent harness excels and you know, lives very closely with uh with the data layer. So, let's focus on the on the data layer. The data layer is where an agent harness appears. And it has, you know, many components. The first component is the gateway and MCP. It's interesting because that's the one that connects an agent harness to data and tools. So, kind of think of of a large language model like a isolated thing that wouldn't be able to work at all if it didn't have access to things like data, right? So, you'll you you then have like the memory layer, the semantic layer, retrieval layer, context layer, and tools and skills that are built on top of the gateway and MCP layer. Let's say for instance that you have a I don't know, an application on your computer and your large language model doesn't have access to it. For instance, Outlook. Right? So, if you might want to have your large language model connect to that, you can create an MCP, you specify some functions, and then the LLM all of a sudden is able to communicate with this program. So, gateway and MCP is like the layer that connects a large language model to the outside world or de facto to our computer or wherever you're working. And AI applications today, they take up four different um four different shapes, right? You have LLM chatbots, which are very passive. Uh they just respond when you ask a question. You also have rag applications, which are semi-passive because they have to do some kind of processing in the background, but then you also have a passive nature of it. And then you have the more active components of AI applications, which are what we kind of use every day. Like cloud code, etc., etc., which are a combination of LLM-driven workflows that provide automation and AI agents that provide autonomy. And we'll go we'll we'll explain later what I what I mean by that. But first, I want you to have a very clear definition of what an AI agent is. So, to me, an AI agent is essentially a model large language model plus a harness. The model itself will be the reasoning and everything else will be the harness. Um So, this definition is something like this. An autonomous entity whose cognitive functions are powered by a large language model for reasoning. They are augmented by a database or files for memory. They are extended through tools for actions and grounded in inputs that let it perceive uh its environment. So, an agent is a a model plus the harness. So, if the agent is the model plus the harness, in this diagram, right? We have reasoning and the reasoning is the part that we don't control. It's the part that we are that's that's heavily subsidized, the part that we rent. If uh if you're like me, you're subscribed to every imaginable subscription on Earth. And that's the thing that we do not control. Right? We have no control over what we are offered. And then we have memory, tools, and perception that actually we get some customizab- Um So, the goal of harness engineering is to create reliable and predictable outputs over and over. Whereas a reasoning model is very non-deterministic. You might give it the same the same input and it might produce different outputs every time, right? So, I said that, you know, in AI applications the most typical ones nowadays cloud code, codex uh and any other type that you can think of is a combination of automation plus autonomy. Why? Well, because automation provides reliability to a system and autonomy gives flexibility. And this is a combination that is very convenient when we are developing ourselves. By the way, raise of hands, who is working as an AI engineer or as an AI developer? Oh my god, okay. Good. So, you must all have used one of these systems, right? So, all of them they have this commonality, which is they have autonomy and they have flexibility. Um Yeah, so the idea is that these systems, right? They are built on top of an a proprietary agent harness. And an agent harness is nothing more than everything that we spoken about an AI agent. All the things that you need to do around that to enable it to produce reliable and repeatable outcomes, right? So, the model itself non-deterministic. Same input, different outputs every time. But the harness, what we want to do with the harness is to turn this non-deterministic nature of a large language model and be able to produce reliable and repeatable results. So, the reasoning, which is the part that we do not control, we're not going to focus. Actually, the harnesses are built on top of models that are kind of swappable. You just need a like a common interface like an OpenAI protocol or the Anthropic API specification, right? All these things make it so that the model part is swappable and the harness is what we're going to focus on today. So, seven things that make up an agent harness. And as I said, we're not going to touch on the model layer because we have no control over it. But let's go a little bit more in detail into each of these, right? You need a storage layer on your agent harness that essentially determines where the data is going to live, where the memory physically lives. Uh and we'll see about this dilemma that has been going on about the last 6 months about files versus databases. And why I think a hybrid uh combination of both is actually the best part. Then you also have memory engineering components which are all the uh encoding, the search, and the retrieval components of it. And also the semantic layer which is kind of the the the hidden things that happen or the hidden vocabulary that we assume that a large language model knows that is kind of proprietary to our companies or our knowledge. What we don't say to the LLM. Kind of that's the semantic layer. We'll lightly touch on agent loops and what an agent loop is and how to implement a very very minimalistic agent loop. And finally, go about some context engineering techniques that you know, keep the window as salient as possible, the context window as salient as possible. You want to minimize the context window as much as possible so that that the task that you're solving stays relevant. So, until here we have done an introduction to what an AI agent is, right? Its use cases. And now we're going to dive deeper into an agent harness and each one of the individual components. So, the model layer, right? The frozen reasoning core. I say it's frozen because typically the weights of a model don't change. Uh and I say typically because uh we are actually I'm actually in the process with Cassius sitting right there. We're going to record a new course with Andrew Ng on continual learning for AI agents. So, if you're interested, just check that out in a couple couple weeks. But the thing is that 99.9% of time you will have a model and the weights of the model will never change. Uh unless you have millions of dollars or, you know, a lot of time or GPUs, it's very hard to change the weights of a model. So, there are other ways in which you can affect the reasoning without actually changing the weights of the model. But this is motivation for for the continual learning part that we will see in the workshop. Where does the memory live, right? The files versus databases dilemma that we've been having uh since January kind of. Some people are very maximalists of files and some of us are Oh, I will not include myself. But some of Some people are also maximalists of the database. Um and you know, both things are right. Files have convenient things, convenient characteristics, and also the databases. So, the files, right? They are very easy to like they are very they match the models' instincts. They are very easy to create. They are very easy to insert and append data into files, right? It's very It has a very unstructured nature to it. And databases on the other hand, or the conception that people have, is that they have a very structured way, right? But, that's when you're thinking about SQL, like SQL. And the things that you don't actually need to ch- like choose one or the other. You can actually use both of them. Like files are attractive because the models speak them, and they kind of work very very easily with operating systems. They follow POSIX semantics, so they are compatible on Debian, Ubuntu, any other operating system that you might want. And they also have some disadvantages. For instance, they don't have transactional consistency. So, this is one of the problems that I wanted to talk about. If you are a degenerate like me, and you are working, I don't know, like eight, 16, 32 agents at a time, uh the problem with this is that files cannot be modified and inserted and modified at the same time. So, what is the solution nowadays to not having transactional consistency and working with files? Any suggestions? Work trees, exactly. So, agents, what they when they want to modify a file, but another agent is working on this thing, they just created a different work tree, right? They will do all the progress in the work tree, and then after the implementation is done, they will merge to master, or the merge to main, sorry. So, this is the way that is a workaround against not having transactional consistency, right? And you also have other characteristics, like for instance, hybrid search. Um this is not available on files, but is very very easily achieved on databases. Um you don't have backups either. So, if your operating system gets corrupted or something, you will just lose everything. And these are things that the database fixed 35 40 years ago, and people have kind of forgotten about that. So, what I want to do is to give you kind of the best of each of the of the implementations, right? You can get the benefits of files and the benefits of databases in the same place. And we'll see why. But these are some of the advantages, right? You have ACID consistency. So, atomic operations, consistent operations, isolated, and durable. You have high availability. You might replicate the database and let it be, you know, in three different places in the world with a replication factor. You also have vector search, which is very easy. In files, you just have to do like regular expression matching or a derivative of that. And you know, lots of other lots of other things. So, what we will do on the on the actual workshop is we're going to run an actual example of trying to modify a file with three different agents that will be working on the same file and trying to update a counter on this file. And let's see who's faster. I know the answer, of course, but you will you'll get to know it later. But, what I want to introduce to you is that we have a thing called the Oracle DBFS, or the database file system, where you can store files inside the database on a file system. And that gives you, you know, lots of the advantages that we said. You will get files with ACID transac- transactional consistency. You will get vector search, relations, security, high availability, etc. Right? And my suggestion is that since a hybrid system works best, um we can have things like, for instance, uh short-term memory, right? That lives in files. And when something needs to be promoted into a long-term memory, for instance, user preferences, things like this, they can go into a more structured space like a database. And this is what we'll do in the workshop. For the encoding the search and the retrieval, which was another of the components in the agent harness, we also have uh LangChain integration that I want to mention called LangChain OracleDB that makes it very easy to insert into vector stores, search in the vector store, and retrieve from the vector store. So, also we have another thing called in-database embeddings. Have you ever heard about in-database embeddings? Yes? Okay. So, in-database embeddings is very convenient, especially for enterprise customers, because you will get the embedding model inside the database, so that when you're doing embeddings, you don't have to call a third-party service. And that's very convenient for isolate like data retention and data security purposes. So, this is what a an an embedding searching and reranking model would look like from a chatbot interface, for instance, right? You have a lot of documents, then you kind of use a bi-encoder. So, a bi-encoder is essentially an embedding model. You will create enco- embeddings out of these documents. You will split them and create vectors, put it into a vector store, right? And then you will get a user prompt, a user query, a question. You can also create an embedding out of that and then compare it to what you had previously on your vector store. And this is how you get the most relevant vectors in in an answer. Then you will run an across encoder, which is a re-ranker, and take a look at the question plus the result, and that's how questions are answered kind of in in in rag applications, right? So, these things we are also going to to touch briefly on the workshop. And the thing is that, you know, imagine this rag application from the beginning. From the documents, you create you do lots of things, right? You do tokenization, then you create the embeddings. Um you have to do things like deduplicating the data, normalization, uh personal like redacting personally identifiable information, uh lots of things, right? And then on your on your store, you have textual data from the documents. You have metadata, which is typically stored in JSON. You have vectors, which are represented as as dense embeddings of 32 uh bits. Like you have so many types of data that you need to work on that typically what people have is, for instance, I don't know. I will not name names, so I don't get in trouble, but you know, you might need like different databases for each one of these, right? You you get what I mean? So, there is a very high nowadays, very high data synchronization logic overhead for AI engineers or even for agents, right? So, lots of maintenance required and and and lots of engineering effort on it. And this is something that we want to avoid. So, what I want you to do today is just to try us out as Oracle. Uh try our database. We have support for every type of data imaginable that you can think of. Um we are called the converged database. We are the only converged database in the market that we support JSON, we support relational, we support uh spatial, graph, JSON, anything that you can think of, you'll be able to to create with us. And you'll get like one database engine, uh one query interface, and uh one single development stack. Everything will be in the same database. So, also for data security purposes, you just have to you know, secure and save all your data in one place. So, a single attack vector is what you need to worry about. You don't need to worry about updating five different database. You can just worry on securing one database. So, we can be the engine of your AI applications, not just a single step, which is what people think of when they're working with databases, right? Um Yeah. So, agent memory. Agent memory is essentially a description of all the mechanisms and the systems that allow an agent to retain, reuse, refine, and recall information. We want to reuse the data and refine it in the process. But we want to reuse the data so that the next time that an agent or us as engineers we are working on a problem that took us 3 hours, the next time that we observe this problem, the problem becomes easier either for AI agents or for us. And agent memory has lots of lots of components, right? We have short-term memory, we have long-term memory, and then we have shared memory, which is something that's relatively new. Um and this shared memory is kind of what happens when a sub agent is communicating with it it's communicating with its with its parent, for instance, or two agents are trying to collaborate on solving one specific problem uh together. And then what I want you to to see is that depending on the type of memory on or or the type of thing that we want to store, it will be in one place or the other, right? So, short-term memory is kind of ephemeral, it's very short-lived, and it's very useful for things that are happening right now. For instance, the to-do list on a coding agent, right? It's happening right now, but you actually don't want to save that uh you know in long-term. But then there are things like, for instance, episodic memory, things that previous conversations that you've had. That's very useful to have, for instance. Um I don't know if you if you use Claude. Some people are using Claude here. Uh but in in Claude, you might take your previous conversations and try to refine all your workflows and your skills based on the things that you've done in the past. So, this is something that's makes sense to to save in the long run, right? You also have things like procedural memory. Previous workflows that have worked very well for your system. For instance, you worked on this front end, and then you created a very beautiful design that you like. You might take the whole conversation and turn that into a workflow that is repeatable and re- reusable. So, that the next time you're working on a front end, the results will be similar to the previous one, right? So, these are the things that we we will see on the workshop. And some people say, "Okay, why do I even need all of this?" Like people that are very that have animosity towards agent memory. People say, "Okay, let's just put like 15 million uh context window." Even though it's not not possible yet, but some people really believe that this is the the thing, right? But the context window is a type of short-term memory. So, it's useful for some things, but not for all of them. And one of the problems that that happen with with working with a with a context is this thing called context rot or context degradation over time. And what happens is that the more things that you put into the context window, the less attention there will be for each one of the things that are in the context. So, at the beginning of a conversation, and this is a a a famous problem that that the context window has is at the beginning of the conversation, it will stay on track a lot because you are just you just started the conversation. So, let's say that for instance, like in school, right? Or if I'm having a conversation with you, um I might have the a chat with you for 30 minutes, and your attention to me is very very high because I just started speaking. But if the conversation goes on for 8 hours, then you want to punch me, right? Because I haven't shut up, and you haven't learned almost anything at the end. And the the problem is that attention, like us humans, is very limited. So, the more things that you put in the context window, the attention matrix of the neural network will all also degrade, and it will like scale quadratically because the attention matrix, you know, is one token is essentially a reference of one token for every other token in the context window. So, the bigger the context window is, the matrix scales on the number of rows and on the number of columns as well, which is a problem. So, you want to keep the context window as small as possible to avoid context rot. And memory engineering, the components of memory engineering, um so, it's like designing, building, and doing everything around building agent memory for AI agents. And we want to retain, recall, reuse, and refine this data in some type in some way. So, it is a discipline, right? And here we have Valentin, for instance, and we have people from Oracle, my colleagues all over the the room. So, if you see them, you can say hi to them. Valentin here, he's working on the development or he worked on the development of this agent memory package. So, if you have any questions about this, you can ask him. Um OAMP or Oracle Agent Memory Package is the managed answer that we have in Oracle to lots of the problems that you will find when working with this type of data. For instance, as an engineer, if you do not have a managed solution, you need to make a lot of decisions. You need to see, when do I do context compaction? When do I summarize? How do I write it the summarizer? What do I keep? What do I not keep? Um what do I extract from my previous conversations and when? How many tokens do I use for this problem? And lots of these things, right? With OAMP and Oracle Agent Memory Package, you can actually just do all of these in one single line of code. And we want to make it easier so that we reduce the cognitive load of AI engineers and AI agents as well. And with this context card thing, you will get something like what you see on the left. And this is kind of an explanation of what each part does on it. But essentially, the the topics that you see, for instance, from any conversation, you can create a context card from the thread or from the conversation. The topics will orient the model. The summary will compact the thread and it will state the current intent of an AI agent. And the relevant information has three different parts, which is the the facts, the preferences, and the memories that are associated to this conversation. Uh then you have the episodic memories that explicit explicitly track the unanswered question that is going on right now. And the recent messages give like local context to the model. So, whenever you're feeling like unsure, what do I need to do right now with the data that I have or this conversation, you might use the Oracle agent memory package on on Python and it is all assembled by one single call. Yes. \>\> So, you're not suggesting that this goes directly into the model. This is actually something that is used by the furnace \>\> Exactly. \>\> to harness what's going into the model. \>\> Yes. Yes. \>\> And this is the component that you're talking about that Oracle uses. It knows about the structure and it uses this structure to \>\> Exactly. Exactly. So, this structure This is an abstraction that we build on top of the model because the model we have no control over in most cases, right? Um The thing is that we can build on top of that whatever abstractions we want to make the model perform as reliable as reliably as possible. Yep. So, the memory and the semantic layer they kind of work together with I know we talked only about memory components. Uh I'm going to walk you uh quickly because I'm I don't have a lot of time um to the semantic layer, to the semantic components of it, right? And the semantic layer is the the meaning of what's going on behind it that you kind of assume that that happens, right? So, anyone from Germany? Okay, so I apologize for my pronunciation, but I'm going to try. So, the Umwelt is like the ambient of a model, and this was coined by Jakob von Uexküll. Um and this guy said that essentially every organism in the world uh that is living perceives its reality through a lens. And the lens is what it has access to. For us humans, for instance, we have our eyes, our senses, right? So, everything that we perceive and everything that we live, all our experiences, are seen through this lens, right? And an agent doesn't have human like senses. But it has also a kind of semantic layer or a semantic lens that everything that you ask it is filtered through. And this lens is essentially what you train it with and then what you also give context to. So, everything that you talk to the agent, right? When you talk to the agent, this agent will look it through the Umwelt. And the semantic layer is essentially the agent's Umwelt. So, the Unsaid is what what what I want to focus on. Like, for instance, organizational knowledge or enterprise knowledge. Things that when you're working with a colleague you don't mention this because this is already you know, known between you and your colleague. You don't need to specify everything that you work on every day, but if there was someone else, like a a child, that wanted to that \[clears throat\] wanted to start uh working with you you would have to specify everything very very in detail. These are all the Unsaids, and this is what the semantic layer captures. So, the Unsaid is the actual tribal knowledge that that an an enterprise has, right? Institutional knowledge as well, like how the data is modeled, how the queries are executed, what is the metadata. All these things are in the semantic layer. Um and quickly just for you to know that we're also going to implement a very minimalistic agent loop. And an agent loop is like the driver of the model, right? It lets a model be kind of independent and autonomous. And it is what makes a model it it it turns a model into an agent, right? And this is like the simplest agent loop that you can find. It's kind of this observing and reasoning and then acting part that happens all of the time, all of the time. And you know, it's it has to be failure failure resistant so that we never exit the loop. This is the idea. That the agent you give autonomy to the agent. Well, you give autonomy to the model so that it becomes the agent. And then on the context engineering part, which is the last part of the seven layers of an agent harness, you also have lots of things that you can do. For instance, the toolbox pattern and the skillbox pattern, which we are This is on the workshop as well. And these are ways in which you can store the available tools and the available skills of a model so that they are retrieved optimally. And what you want to do is only retrieve the tools and the the skills when they are actually needed and put it on the context window only when needed. Every iteration of an agent loop, you will see if this is actually the right place and then if it's not, you can just take them out temporarily. So, uh Oh, sorry. I thought I I saw a question. So, we will see all of these in the workshop. I don't want to take up too much time. But, the idea is that we will assemble the context at every iteration on the agent loop. And then, continual learning is the part that that we talked before about the ability to get better over time with the things that we've done with the model, right? So, a frozen model, as we saw, it doesn't get better, right? But, there are ways in which we can make an agent improve in the weights, which is the part that we are going to work on. Uh, in the representation part, so on the embedding and the re-ranking part, and also in the context window. And these are the three types of continual learning techniques. We are going to focus on the workshop on the context and the token space because it's one of the easiest ones and all one of the least expensive ones, as well. I assume no one is a millionaire or not many of us are millionaires. So, this is also the most achievable and the most realistic way to change the model behavior over time. So, without further ado, I just want to introduce you to this part, which is skill promotion and workflow workflow promotion. So, those skills or those workflows that you've done for 3 4 hours, right? You've been working for the whole day on a workflow. You were able to successfully do your job, um, and then these things can actually be retrieved. They can be stored into the memory components that we'll see. And then, we will see about skill promotion. So, if we promote a skill, for instance, we can promote it through a distillation process and create a better skill.md than the original. So, we will retire the old version and update from the new version. And this allows us to do some kind of continual learning on our own skills that turn them into more customized skills for ourselves, for our tone, our way to work, our preferred like our preferences like for instance, let's use this specific library because I really like the look and feel of it. Let's use this specific database engine because it has less bugs or I found it easier to work with. All these things can be promoted into reusable and improvable skills over time. So, this is what the whole harness would look like at the end of the of the workflow. Sorry, at the end of the workshop. And hopefully what you leave this you'll leave today with a better understanding of all the specific components that make up an agent harness. So, let me go to here before I forget. If you are interested in the Oracle agent memory package or are working on the agent memory package, we have a Discord server in which you can just chat with us as staff. If you're a Discord user, just feel free to to join this Discord server. I'll put the link later as well. But, without further ado, let's begin with the actual workshop and I will tell you how. So, let me show you first what we are going to build, right? This is an upbook. Oh, sorry. Uh you don't see this. Yeah. Yes. Uh the the the first Yes, the workshop instructions, right? So, for those of you who weren't here, you can just go into this website, register with your GitHub user, and then you will get an invitation like this to a GitHub repo. And from here, we will create a GitHub code space. Uh so, please make sure to do that right now. And all my colleagues are are around the room room to answer any questions that you might have during the creation of the code space, etc. Yes. Yeah. The internet? Are you having issues with the with the internet? Okay, let's see. So, do you guys have my the same Wi-Fi? Um ai.engineer Wi-Fi? Okay. So, can someone assist people with the Wi-Fi if possible? Yeah. Please fix the system, whoever. Do it. I can't I can't. Yeah. Yeah. Uh my colleagues will will take a look at that if Right. Yeah. Yeah, it it always happens, you know? Yeah. All right. Say again, sir? Yes, they will go into the air I \>\> believe they will they will go into the AI engineer. So, if you go into the session uh I'll make sure to to to go there. If not if you either join the Discord or any other, you know, you can just message me as well on LinkedIn. I'll gladly give you the the slides if you want. All right. Um video team Can you turn me on? Uh sorry, switch me on. Please. Uh Oh, perfect. Thank you. So, what I'd like to to show you is that we have built also, apart from the notebook that we're going to go through we also built this app book. And with the app book, you can actually test every of the individual components of an agent harness uh individually, right? So, just for me to show you that this is possible. And you will get this automatically deployed in GitHub code spaces as well. So, you will have um this uh already deployed. And you'll say, "Well, how am I actually making requests?" We are going to be making requests to uh Oracle uh oh Okay, not fun. Demo time. Um the idea is that Yeah, okay. I I know what's happening. So, I lost connection to my to my code space because of inactivity. Let me restart. Uh this app book is going to allow you to create and chat and interact with the whole agent harness. And the models that we're going to use are actually deployed on a managed service that we have on Oracle called OCI the generative AI service. We have partnerships with Google, with Meta and with OpenAI and with xAI for the time being and we can actually provide inference to their models through our managed uh service. So, just think of us as the enterprise open router, if you'd like. So, let me just go so you get started uh you can get started. This is the repo, right? So, the agent harness workshop. If you're here, um thank you for starring that, by the way. Um if you're here, you just have to click on open in GitHub code spaces. And it will take you here. And you can select as many cores as you like. Uh if you So, if you want to create this with eight or 16, please don't because I I don't I'm paying for this myself. But uh you might also create this with more resources. But just create the code space. And I'm going to pay for it, as I said. So, don't worry about that. And this will create a new a new code space instance. And once it finishes, which it hasn't yet, I will show you what we can do with the app book and the notebook. But the idea is to use the remainder of the time the we have 1 hour and 15 minutes to go through the notebook. And you will actually have to Let me show you on on GitHub, actually. Um you can go here. And inside the notebook, after you deploy the code space, you will get a a student notebook here. And this is one part of the of the workshop, right? And here we're going to implement the whole agent harness substrate from scratch. So, we're going to start with only the model, and then we're going to keep adding layers to the agent harness, as we saw the seven layers, right? And we're going to be here to to assist you. You will have to do some to-dos. So, let me show you. There are a couple of things to do for you. So, for instance, the first thing that you need to do, you need to create a question, right? The simplest thing of everything. You just have to communicate with a model with no agent harness implemented, right? So, the first thing you'll need is to ask any question that you like. This will go through the OpenAI completions API, and it will return you a response. This is the simplest of all. And then, we will start adding search, retrieval, encoding, and all the other components that we that we have seen. There are a total of 19 things that you need to do. If you finish first, raise your hand, and I will give you a hug, because I don't have anything else. And yep. So, anyone already deployed that the code space? Okay, one person. Okay, good job. So, any Yeah, if you have any questions or any problems, uh let me know. But, this is what it looks like when you have it deployed, okay? So, let me go through this quickly. So, you will get an app, right? Um and the app will already have everything that you need. If you want to deploy this app yourself, you might change this total recall port here. Let me show you how I did it again. I go into ports. I clicked on the visibility of the port, and I changed this to public. And then, this is now using a public gateway so that if I open the browser, I can actually get access to my individual total recall instance. So, for instance, if I ask a question like show the total revenue by product category. And of course, this is mission control, so this is this has all of the components that we've spoken about implemented already. You will get also a context window visualization of the things that are going on on the background. For instance, these are the tools that were selected by the agent harness to be loaded into the context to answer this question. This is the schema that's happening, and then we can also take a look at the individual agent traces that are going on. For instance, which skills which skills are being loaded, what sources of data are we taking and what are the tool calls being used like for instance running SQL commands etc. to answer your question. So, the question is still being built. It's taking 16 steps. And you know, it's going to for instance here, it detected an error, right? But because our agent harness is uh fault tolerant, it will keep trying because it's it has an agent loop implemented etc. right? So, all these things will actually yield you this result uh from from the data in the database, right? And you can actually go into the context window, see how many tokens were using. And if you're particularly interested in some of these parts for instance, the Oracle agent memory package for instance, you can interact also with only the the context card, how to how the context card is being created etc. etc. So, you will all get this deployed in your code base. Yeah. \>\> So, you showed that the harness is choosing most \>\> Yeah. So, his question for those of you who didn't listen where what happens if you have thousands of tools in an organization, right? Well, we introduce this concept called the toolbox pattern in this course with Andrew Young. And the thing is that you can optimize so that the retrieval of these tools is negligible. So, you will use hierarchical navigable small world indexes that use a graph a graph structure and then each node in the graph is a vector index or a vector store. And then you can actually like HNSW indexes, they can be created created for these types of problems only in the data is not not in files. So, great question. It doesn't have to worry you until you reach millions and millions of users. And tool calls like different specific tool calls, you might not get 5 million. It's more like reading a file, writing a file, grabbing and all these kinds of tool calls that that that we do every day, they typically don't excee- like exceed 100 or 1,000. Um but by being on a vector store, you abstract the complexity the complexity and the amount of it. You can just make a query to 1,000 just as simply as you would 10,000 because of the storage component that we we choose it, which is an HNSW index. \>\> So, let's assume that maybe two different organizations within the organization, business units. They have access to something let's say like \>\> Yeah, some of them they have access to confidential data for instance. Some of them don't. Yes. Great question. So his question was what happens if the tool descriptions that two different companies have are very similar, right? And one of the things we we can do on the toolbox pattern is actually generate with LLM enhanced toolbox descriptions for this for specific tools to increase the separability of the of the tools. So if you think that the current descriptions of a tool or of a skill as well are not enough, you can actually enhance them with LLM retrieval like you would uh instead of running for instance named entity recognition, which is very uh caveman style, you can also do something more sophisticated, which is enhancing the the kind of like docstring uh enhanced representations of a tool so that you increase the separability when you're doing vector search. Does that answer the question? The agent loop? Is that Oh, yes. Yes. That's Yeah. In your harness, how do you Yes. So, there there is a there is a limit, of course, because we don't have infinite money. So, we can't just keep trying and trying over and over if the generations are just hallucinations, right? There is a cutoff point that I set depending on the frontier LLM that I'm using. For instance, for 4.1 fast reasoning, which is the one that we're using here, I found that a value of 8 to 12, like maximum number of tool calls before giving up, is correct. Depends also on the on the accuracy and the and the correctness of the model. Like, for instance, in this case, it was just able to to to to show it in two. Before, it was able to find it in 16. So, sometimes it will have a faster retrieval. Sometimes, you will need to be a little bit more patient. But, what I like to define is a variable like a hysteresis variable that holds the amount of patience that the harness will get with the model. Then, you can do some other things. Like, for instance, if the model is garbage, you can just use or use it like a router for more different like for difficult types of problems, you will route this problem to a frontier LLM. And then, for the easier types of problems, you can just attach an open weights SLM, for instance, which will be more more interesting. Yes. Yes. I think that's Like, my personal opinion is that the future is mixture of small experts for for each type of problem. Some companies that they have developed like 100 million parameter models that work exceptionally well for one type of problem. And if you just have an aggregator and an orchestrator that routes the correct model to that like the correct query to that model, then you will have a very token efficient type of agent harness. So you you you can actually do model routing inside the agent harness. Some companies are actually essentially only doing that and they will charge you like let's do I'm going to charge you 10% of the tokens that I'm going to save you from the original amount of money that you were going to spend. Right? Uh so let's let me show you the the student notebook, right? So once you are inside the student notebook for those of you who are not familiar with Visual Studio Code, you might need to select a kernel here so that you run the the notebook. So you might select Python 3.12 here and then you can just start reading. If you stumble into a to-do that you need to do, you have a docs folder with all the explanations, the individual explanations that you need to solve this specific problem. For instance, the first to-do, which is just talking to the reasoning core, to the model layer without doing anything else, will just explain what you need to implement on that cell so that it works and you can proceed to the next one. And also have the solution. But if you if you're not lazy, you will try. And I hope that you that you try. And we will be here answering questions around the room. I'm going to turn off my microphone, just come down with my colleagues, and then let's chat about it for the remainder of the of the session. And if you have any questions or you like to talk um more to us, please come by and and swing by the booth, the Oracle booth. We'll be there every day uh all the time. And you know, it makes it makes us feel good like we are wanted and we have friends. So, if you want to come up to us, just chat with us a little bit. It It will be nice. Internet, how's the internet? I feel like Caesar. All right, so I'm going to leave this here. I'm going to keep this here and I'm going to come down. \>\> \[music\]