Conference talk by Sonny Merla, Mauro Luchetti, and Mattia Redaelli of Quantyca on building an enterprise registry for MCP servers and A2A agents. Topics include an internal MCP registry based on the open source specification, an A2A agent registry using agent cards, runtime agent discovery via an MCP server exposing company A2A agents, and linking agent and MCP server template repositories to DevOps processes — moving from fragmented AI tooling to a searchable, metadata-rich, standardized deployment infrastructure. # Video Identity Observations — "One Registry to Rule them All" ## Video Overview A **conference-style talk** featuring three speakers: Sonny Merla (Global Data Science and AI Manager, Amplifon), Mauro Luchetti (AI Center of Excellence Manager), and Mattia Redaelli (AI Engineer at Quantyca/Quantica). The presentation describes Amplifon's enterprise AI governance platform ("Amplify program") built around registries for MCP servers, A2A agents, and AI use cases. ## Explicit Self-Description Evidence ### Speaker Introductions - Sonny Merla opens with: > "Hi, I'm Sonny Merla, Global Data Science and AI Manager at Amplifon, and I'm here today with Mauro Luchetti, AI Center of Excellence Manager, and Mattia Redaelli, AI Engineer at Quantica, the team that design and build the technical solution that we are about to describe." - Organizational self-description: > "Amplifon is the world leader in hearing care solutions. We operate across 26 countries around the globe. We are more than 20,000 people, and we operate over 10,000 stores across the globe." ### Framing of the Session's Purpose - Stated goal: > "Today, we are going to show you how Amplifon tackled down this problem by launching their own Amplify program, and specifically how we design an enterprise-grade registry system for MCP and A2A agents." - Explicit acknowledgment of the venue context: > "I don't want to obviously tell anybody what MCP is not at this conference" ## Intended Audience (Observable Presentation Evidence) - **Enterprise AI practitioners and conference attendees** familiar with agent protocols (MCP, A2A, agent cards) — assumed baseline knowledge, no basics explained. - **Developers and platform/engineering teams**: significant time on blueprints, CI/CD, Docker, FastAPI, GitHub template repos, observability (LangFuse), and developer experience: > "The idea is that these two blueprints actually have boilerplate provided and also infrastructure and tooling already present." - **Governance/AI leadership audience**: extended discussion of operating models, control tower, committees, compliance, and lineage. - **Peer organizations**: closing invitation suggests an audience of counterparts: > "feel free to reach out to us and keep in touch if you have any similar point of view or also something different that you want to discuss, more than welcome." ## Claimed Scope (What the Presentation Claims to Cover) - A **global, cross-functional AI adoption program** launched January 2025 ("Amplify"), with three pillars: **governance, platform, and factory**. - A platform comprising: - An **AI gateway** (unified endpoint, auth via Entra ID, budgeting, auditing/monitoring) - **Three registries**: MCP registry (built as extension of the public community MCP registry), A2A agent registry (agent-card based, auto-published via CI/CD), and a use case registry (linkage/lineage). - A **live product walkthrough** of the platform (home page, catalog, inspectors, lineage view, widgets). - **Developer enablement artifacts**: two GitHub template repositories, framework-agnostic A2A blueprint. ## Demonstrated Presentation Patterns (Observable, Not Claimed) - **Multi-speaker structure**: Merla covers business/strategy; Luchetti covers architecture; Redaelli covers developer tooling — an observable division of roles. - **Case-study format**: problem ("dozen of teams across three continents... You get chaos") → organizational response → technical solution → demo → results summary. - **Honest hedging about maturity**: > "Of course, it is still in progress the work on this platform, so we are keep growing the capabilities." And during the demo: "Here we have demo data." - **Client-vendor collaboration model** observable in the speaker lineup: Amplifon (business owners) presenting alongside Quantyca/Quantica (technical builders). ## Notes for Synthesis - No channel-level metadata was supplied; identity inference is limited to this single video's content. - All self-descriptions are first-person creator claims; the demo walkthrough is the only directly demonstrated capability evidence (shown with demo data, on a platform "going to deploy... soon" per the walkthrough). # Delivery Observations: One Registry to Rule them All ## Overall Format - **Multi-speaker presentation**: Sonny Merla opens with business/organizational framing, hands off explicitly — *"So, now I let Mauro to introduce how we address these topics at Amplifon"* — then a third speaker covers the demo and development cycle ("Okay, so we wanted also to give you a brief overview of our platform"). - **Rhetorical question opener**: The talk begins with a hypothetical scenario — *"What happens when you have dozen of teams across three continents all building AI agents...?"* — answered immediately with *"You get chaos."* ## Vocabulary & Technical Register - Heavy use of **enterprise AI terminology**: "registry," "governance," "lineage," "use cases," "blueprints," "CICD," "agent card," "operating model," "control tower." - Repeated abstract-but-precise phrases: - *"enterprise-grade registry system for MCP and A2A agents"* - *"avoiding to reinvent the wheel every time"* (Sonny) - *"make the life of developers easier"* — appears in variants across speakers: *"make easy the life of developers"* and *"in order to make the life of the developer easier"* ## Teaching Habits - **Enumerated structure**: Both early speakers favor triads and numbered lists — *"We have three of them, governance, platform, and factory"*; *"ownership... environment... authentication model... cost attribution... use case linkage."* - **Signposting transitions**: Frequent pivots like *"So, let's go in a walk through"*, *"Let's see now how it works in practice"*, *"Let's try to go in more detail about each of those registry."* - **Explain-then-justify pattern**: Features are listed, then their business value is stated — *"And these are not simply, you know, metadata that are nice to have. This is something that really bring out the impact analysis functionality."* - **Guided demo narration**: The walkthrough speaker narrates UI actions step by step: *"if we open a sample use case for instance, we can define its status, its version, its description..."* ## Verbal Habits & Disfluencies - **Filler density increases notably in later segments**, especially the third speaker: repeated "uh," "um" — *"Uh the idea is that uh once you are ready with your development, uh you can tag um a certain branch"*. This is a stable pattern in that portion, not isolated. - **Immediate word/stutter repetition** throughout: *"the the the"* appears multiple times across speakers (e.g., *"the the the internal Amplifon team"*, *"connects connects all those all those information"*), suggesting live, unscripted delivery. - **"You know"** is a recurring filler, mostly from the second speaker: *"you know, the public community-wide catalog"*, *"you know, the custom internal servers."* - **Self-corrections mid-sentence**: *"For who don't know Amplifon"*, *"which is which team, which use case"*, *"how I can effect how I can as a developer use that server."* ## Speaker Differences (observable) - **Sonny (opening)**: most structured, least disfluent; organized framing with clear program pillars. - **Mauro (architecture)**: uses "you know" and parenthetical asides; occasional audience-aware humor — *"I don't want to obviously tell anybody what MCP is not at this conference."* - **Demo/dev speaker**: highest filler rate; consistent "So" sentence starters and concrete, tool-level specificity (Docker files, FastAPI, GitHub Actions). ## Uncertainty Notes - Speaker attribution for the demo and CICD segments is inferred from handoff context, not explicit name introductions; the transcript labels only Sonny and Mauro by name. - Emotional tone, personality, and motivations are not assessable from this transcript and are not claimed. # Position Record: "One Registry to Rule them All" — Sonny Merla, Mauro Luchetti, & Mattia Redaelli (Quantyca / Amplifon) ## Context & Framing - **Sonny Merla** (Global Data Science and AI Manager, Amplifon) opens with the problem statement: dozens of teams across three continents building AI agents independently — each wiring their own connections, security models, and infrastructures — produces **"chaos."** - The talk describes the **Amplify program** at Amplifon and, specifically, an **enterprise-grade registry system for MCP and A2A agents**. - Speakers: - **Sonny Merla** — Amplifon, program/problem framing - **Mauro Luchetti** — AI Center of Excellence Manager, Quantyca; technical architecture - **Mattia Redaelli** — AI Engineer, Quantyca; platform demo and blueprints - Note: the transcript attributes Quantyca affiliation as "the team that design and build the technical solution"; Sonny describes himself as Amplifon. ## Company Context (Merla) - Amplifon: world leader in hearing care, **26 countries, 20,000+ people, 10,000+ stores**. - Currently in AI transformation: experimenting with AI solutions; challenges include building solutions **stable over time** and scaling them **responsibly according to centrally defined guidelines**. ## The Amplify Program (Merla) - Launched **January 2025**; global, cross-functional program setting "the rules for the AI adoption," composed of an **operating model** and an **execution plan**. ### Operating model - **Control tower**: a limited group (including chiefs) deciding guidelines for security, legal, technology, and strategy / prioritized use cases. - **Committee**: responsible for running strategy in countries and corporate side; prioritizing use cases more granularly to release value. ### Three focus areas 1. **Governance** — alignment with AI regulation, central strategy/guidelines; making people aware of the program and its rules; communicating value rollout. 2. **Platform** — setting up certified infrastructure and ways of working to scale AI applications. 3. **Factory** — development teams rolling out solutions to market, caring about rollout across countries, building solutions **scalable and reusable across domains**. ### Problems identified (Merla) - **Maintenance and operations**: short LLM lifecycle means needing to act promptly on model disruptions across use cases. - **Governance and compliance**: needing to know where AI is used organization-wide (also for regulation), having a catalog, understanding assets per use case, creating **information lineage**. - **Enterprise scaling**: multiple teams on different infrastructures; goal is centralized governance with clear, reusable guidelines, so developers focus on **business logic** rather than reinventing security, deployment, maintenance. ## Technical Architecture (Luchetti) ### AI Gateway (first component) - **Unified access**: single endpoint for all models in Amplifon's catalog. - **Security**: authentication via **Entra ID integration** ("intra ID" per transcript) to use models. - **Budgeting**: per-use-case budgets (monthly, weekly, etc.) that erode with use and show remaining amounts to developers. - **Control**: central auditing, monitoring, and analysis over all LLM requests/responses. - Position: the gateway is "the top layer" and entry point. ### Three registries 1. **MCP registry** — central catalog of all available tools/integrations exposed to LLMs. Built **on top of the official community MCP registry**, as a private Amplifon registry adding enterprise context. Contains: - Custom internal servers (team-built for specific systems/integrations). - A **curated set of public servers approved/certified by Amplifon** for Amplifon use cases. - Enriched with enterprise metadata: **ownership** (team/use case/project), **environments** (dev/test/prod), **authentication model**, **cost attribution** (linked to gateway budgeting), and **use case linkage**. - Claim: these are not "nice to have" — they enable **impact analysis, governance, auditability**, giving a complete trail of what AI tooling exists and how it's used. 2. **A2A registry** — full catalog of available agents, based on the **agent card standard** (identity, endpoint, capabilities, modalities, authentication requirements). On deployment, agents **automatically publish agent cards via CI/CD**; other agents/developers can discover and interact with them. Claim: this makes agent development **self-documenting**. 3. **Use case registry** — connects the other registries' metadata; maps agents and tools to specific business use cases; records assets used, models, deployment location, and impacted systems; enables lineage visible in a catalog for everyone. ## Platform Walkthrough (Redaelli) - Platform pages shown: home, catalog (MCP, A2A, use cases), AI gateway section. - **Use case pages**: status, version, description, assets used (agents, MCP servers, AI models), life cycle history; creation form with name, description, status, ownership, linked assets. - **AI tool section**: MCP server **server.json** displayed; A2A **agent cards** with capabilities/description. - **Inspectors**: launch an MCP inspector in another tab to check what a server provides; equivalent compatibility check for A2A agent cards. - **Widgets**: forms to generate server.json and agent cards with preview, instead of starting from raw JSON in a repo. - **Lineage view**: e.g., a "ticket optimization with AI" use case connected to agents and AI models; enables identifying affected use cases when parts of the lineage hit an outage or problem. ## Enterprise Development Cycle (Redaelli) - Two **template GitHub repositories** (MCP and A2A) with boilerplate, tooling, Dockerfiles, package managers; both are **FastAPI servers** exposed the same way. - **Authentication and cost tracking handled inside the blueprint**. - Integration with **Langfuse** ("LongFuse" per transcript), an observability tool deployed at platform level, for tracing agents, running evaluations, checking performance. - Claim: the A2A blueprint is **framework-agnostic** — not tied to LangChain, Agno, or others — composed of **interfaces and ports** so teams implement in their framework of choice while providing the same interface; goal is letting developers focus on the agent's actual value. - **CI/CD**: tagging a branch triggers a GitHub Action that publishes the Docker image to the artifact repository **and** publishes metadata (agent card for A2A; server.json for MCP) to the backend/proxy of the catalog. - **Routing**: agents calling MCP or A2A go through the **AI gateway**; the MCP and A2A proxies look up the catalog to retrieve the actual backend URL; the agent authenticates to the server with another header. ## Claimed Outcomes (Merla, closing) - A **catalog enabling governance** of MCP and A2A servers across teams. - **Full traceability** of use cases, agents, tools, and models. - **Production-ready blueprints** giving teams a standard starting point focused on business logic. - **CI/CD pipelines** tying deployment to registry metadata publication. ## Qualifications & Open Items - The platform is shown with **demo data** ("six entities defined"); Redaelli states the platform is "going to be deployed in production soon" during the demo — i.e., not yet production at time of recording. - Merla explicitly qualifies: the work is **still in progress**, with capabilities continuing to grow; invites the audience to reach out with similar or differing viewpoints. # Speaker Attribution Record ## Video **Title:** One Registry to Rule them All — Sonny Merla, Mauro Luchetti, & Mattia Redaelli, Quantyca **Video ID:** `01a15ca9-b6fe-7721-bed4-e550822229bf` ## Identified Speakers and Roles | Speaker | Role | Evidence | |---|---|---| | **Sonny Merla** | **Presenter / Creator-side principal speaker**; Global Data Science and AI Manager at **Amplifon** (client organization) | Self-introduction: *"Hi, I'm Sonny Merla, Global Data Science and AI Manager at Amplifon"* | | **Mauro Luchetti** | **Presenter / Guest-technical speaker**; AI Center of Excellence Manager at **Quantyca** (solution vendor) | Introduced by Sonny; delivers technical registry/gateway sections | | **Mattia Redaelli** | **Presenter / Guest-technical speaker**; AI Engineer at **Quantyca** | Named in intro; delivers platform walkthrough and development-cycle sections (voice attribution within transcript not individually timestamped) | > Note: The transcript does not contain explicit speaker turn markers; attribution of middle/later sections to Mauro vs. Mattia is inferred from handoffs ("Thank you, Sonny", "as Mauro said before", "Mauro talked about the CI/CD"). Uncertainty is retained. ### Role Summary - No **narrator** or **other** roles present. - All three speakers appear as co-presenters of a conference talk (a conference setting is referenced: *"not at this conference"*). ## Organizations Referenced - **Amplifon** — world leader in hearing care solutions; 26 countries, 20,000+ people, 10,000+ stores (self-described). - **Quantyca** (spoken as "Quantica") — team that designed and built the technical solution. ## Referenced Sources / Third-Party Claims - **Official community MCP registry** — starting point for Amplifon's private registry. - **Agent Card standard** — used by the A2A registry. - **GitHub** — template repositories for MCP and A2A blueprints; GitHub Actions CI/CD. - **LangFuse** — observability tool deployed at platform level. - **LangChain, Agno** — frameworks cited as examples (blueprint is framework-agnostic). - **FastAPI, Docker** — technologies in the blueprints. - **Amplify program** — internal Amplifon program (Jan 2025); internal claim, not independently verified. ## Promotional Segments - **None flagged.** Closing invitation *"feel free to reach out to us"* is a contact request, not product promotion. No sponsor or product-purchase promotion detected. ## Availability Limits - **No curated metadata supplied** beyond the title (which establishes speaker names and Quantyca affiliation). - **Comment availability:** not indicated in source material. - **Speaker turn boundaries/timestamps:** absent from transcript. - Demonstration used **demo data**; platform noted as *"going to deploy in production soon"* and work *"still in progress"* — per speakers' own statements. What happens when you have dozen of teams across three continents all building AI agents, each one wiring up their own connections, reinventing their own security model, deploying their own infrastructures? You get chaos. Hi, I'm Sonny Merla, Global Data Science and AI Manager at Amplifon, and I'm here today with Mauro Luchetti, AI Center of Excellence Manager, and Mattia Redaelli, AI Engineer at Quantica, the team that design and build the technical solution that we are about to describe. Today, we are going to show you how Amplifon tackled down this problem by launching their own Amplify program, and specifically how we design an enterprise-grade registry system for MCP and A2A agents. For who don't know Amplifon, Amplifon is the world leader in hearing care solutions. We operate across 26 countries around the globe. We are more than 20,000 people, and we operate over 10,000 stores across the globe. We are in the AI transformation. Right now, we are experimenting with AI solutions, technologies, and so we are facing challenges like building solutions that are stable over time, and understanding how to make them scale responsibly accordingly to guidelines that are defined centrally. So, how Amplifon decided to adopt AI at scale? We launched in January 2025 the Amplify program. It's a global and cross-functional program designed to set the rules for the AI adoption. And it is basic basically composed by an operating model and an execution plan. The operating model is based on two main sources, the control tower and the committee. The control tower is a limited set of people including chief decide deciding what are the guidelines for security, legal, technology, but also what are the strategy the the focus for the strategy, and so the use cases to develop us first. Then there is the committee that has the responsibility for running the strategy in the countries, but also in the the corporate side. So, prioritizing also the use cases more granularly, those are to to release the value to the organization. Which are the main focus of the Amplify program? We have three of them, governance, platform, and factory. So, the governance, we want to ensure the alignment with AI regulatory, also the strategy and the guidelines that we define centrally. So, it's also a matter of make the people aware of the existence of a program, the also the rules of the play, and also then make all the people informed of how we deliver and roll out the value. Then there is the platform side. So, we have to set up the infrastructure on which we operate as developers and implementation teams. So, certifying infrastructures and way of working to deliver processes and also services to scale AI application. Then we have the the factory. So, this is the most practical part of the story. So, we have the development teams that needs to have a focus on rolling out solutions to the market, also caring about the rollout across countries that is very important for for Amplifon. So, thinking about the solution as a scalable scalable and also reusable across different domains. So, which are the main problems that we see as an organization that tries to roll out AI at scale in a pervasive way in the organization? So, we foresee for sure maintenance and operations problems, governance and compliance problems, but also enterprise scaling. So, how the developers needs to develop to make the the solution stable over time. Um Starting from the maintenance and operations side, uh even the short life cycle of the LLM models that are at the core of the AI application and the AI agents that we develop and roll out, we want to be sure that we are able to address the the usage of this kind of LLM LLMs across the the use cases that we we roll out. So, we want to be ready and prepared to act promptly every time we we see a disruption in the model we use in the use case. Um on the other side with the governance and compliance, we we need to be sure to know about where we use AI in the organization, what are the main use cases, also for the regulatory point of view, but also for the usage across the organization. So, we want to have a catalog. We want to have a way to understand also the assets used by the single use cases. So, what they implemented, what they used to also create a sort of lineage of the information. On the other side then there is a a point related to the the way we develop AI solution in the organization across multiple teams that operates on different infrastructures. So, we want to make the developments at least in terms of governance centralized. Then so with clear guidelines, reusable also on different infrastructures and different teams. This is the the goal. So, we we want to make easy the life of developers to focus on the business logic inside the use cases, avoiding to reinvent the wheel every time we need to to take to take care about the security, but also the deployment and maintenance of the the use cases. So, now I let Mauro to introduce how we address these topics at Amplifon. Thank you, Sonny, and let's try to bring more technical point of view in this. The first component that we built in order to, you know, let's try try to to address those problems are an AI gateway. First of all, it brings us unified access. So, all the developers that want to use a model, they can use this gateway, and they can point to the unified endpoint, and use all the models that Amplifon has in in its catalog. Then there's a security aspect. If you want to use the models, you have to, you know, connect to the gateway. You have to authenticate yourself, and we have done this with the intra intra ID integration. Then there's a budgeting aspect because obviously Amplifon has lots of use cases, and if a use case came to you and asked for, you know, for budget for using those those models, you can set in this AI gateway a budget. So, a cost monthly cost, or I mean, yeah, you can set it monthly, weekly, and so on, but you can set a budget, and while the developers are using those budgets, it erodes, and it can brings to developers you know, the remaining part of those budgets. So, they can they can control it. And then there's the the the control aspect. So, all the you know, all the requests that are are done through LLM models or responses, all those all the analytics that we need to put in place on top of all the requests are done using a central auditing monitoring and analysis tools obviously connected to this AI gateway. And then for the governance part, I mean, this is the entry point. This is the top layer, I would say. And then we have three different registries. The first one is the MCP registry. So, as you as you can imagine, all the tools, all the integration with Amplifon systems, all the functionalities that we want to provide to LLM models are exposed through this MCP registry, which is the the the central catalog of all all available tools. Then there's the A2A agent-to-agent registry. So, it brings it's a full catalog of all implemented of all available agents, and it uses agent card standard. It exposes agent card, and also it can it can give the developer the ability to connect to already developed agents. And then there's the use case registry, which is the you know, the registry that connects connects all those all those information together, all those metadata together, and bring out the real governance functionality, the lineage functionality, and again connects all those aspects together. Let's try to go in more detail about each of those registry. I don't want to obviously tell anybody what MCP is not at this conference, but we started from the official MCP registry maintained by the community. This is the you know, the the public community-wide catalog of all available MCP servers, and we essentially build on top of that. So, Amplifon has built its own private MCP registry as an extension in functionalities, and also in you know, enterprise context that we want to add to each of the registered servers. It contains two main things. As you can imagine, the you know, the custom internal servers that the the the the internal Amplifon team have built for specific system, specific integration, specific tool that Amplifon want to provide, and also a curated set of public server that have been approved that have been, you know, certified by Amplifone for for for Amplifone use cases. And both these servers that we want that we register in in this catalog are enriched with some additional enterprise metadata. Let's let's see what are those metadata. First of all, the ownership. Each server has an owner. So, which is which team, which use case, which project is in a way owner is responsible for that specific server. What are the environment in in which the server is running. So, it is running is it running in dev, test, prod, and so on. What are the authentication model? So, how I can effect how I can as a developer use that server? What are the mechanism that I need to put in place? The cost attribution. So, this is linked to the AI gateway functionality, the budgeting aspect that we have described before. And this is done in order to see what server spending what essentially. And then the use case linkage. So, what are the use cases that are effect that are actually using that specific that specific server. And these are not simply, you know, metadata that are nice to have. This is something that really bring out the impact analysis functionality. This is where effectively we enable the governance and the auditability. And we have the complete trail of what AI tooling exist and how they are they are being used by Amplifone developers. And then we have second registry which is the eight way registry. This is fully based on the agent card that, you know, describe the agent's identity, its endpoint, the agent capabilities, the supported modalities, authentication requirements, and so on. We have built some blueprints and then we talk about those blueprints. But essentially, when an agent is deployed, it automatically publish publishes its agent card to the registry via CICD integration. So, in this way any other agent, any other developer can discover this new agent and obviously can interact with it. So, in a way we are trying to make all those agent development self-documenting. Now, we will see how use case registry connects those two other registry together. How do we can use the MCP registry and eight way registry from a business point of view? We want to have a use case registry. So, to map the agents and the tools in specific use case adopted across the organization. And this is the reason why we designed this specific building block that aims to contain the information of what are the assets used by the single use case, what they implement, what are the models they used also for the maintenance topic that we mentioned before. And also understand how and where we develop and deployed this kind of use cases. For example, which is the system that serve the use case the specific use case and what are all the other impacted by this use case. So, for example, if we have connection among multiple use cases, we want to see that clearly in interface that can be a catalog for for everyone. Let's see now how it works in practice. So, let's go in a walk through of the the platform that we developed and that implement all these registries for the organization. Okay, so we wanted also to give you a brief overview of our platform. Here you can see that is the home page. You can go into the catalog, so what we described in detail before, so MCP, eight way, use cases. We also have the AI gateway part where we define what LLMs are available in the enterprise right now. Going back to the dashboard, if we move on to the catalog, this is the platform that we are going to deploy in production soon. Here we have demo data. So, we have six entities defined until this point. We have use cases, MCP, and eight way agents. Going into the use cases part, if we open a sample use case for instance, we can define its status, its version, its description, assets used. So, for instance, an agent and then MCP server, what AI models it's using, and the life cycle history of the of the use case. If you go on to the create use case page, you can see that we can define a name, a description, the status of the use case, the ownership, and also the assets linked to that. If we move to the AI tool section, so MCP servers, you can see that we have two sample MCP servers. So, the actual server JSON is is described here. We can also see into the eight way agents the same thing for agent cards. So, for instance, here we can define the we can have the long chain test agent that test these capabilities and description from the agent card. We also define the inspector page where you can select an MCP server and you can launch the inspector in another tab. So, you can also connect and check what that MCP is providing you. We also have the same inspector that is just checking for compatibility with the eight way agent card. And you can do the same here. We also have widgets. So, you can in order to make the life of the developer easier, define the server.json for MCP and the agent card for eight way with a form and then preview it here instead of starting from the actual JSON on the repo if it's the first, let's say, server for you. We can also check the lineage because, for instance, you can go on to the use case, open a use case that you want to check, open the object lineage. In this lineage view, you can see that, for instance, the use case here that is ticket optimization with AI is connected to an agent, is connected to another agent here, and also has AI models connected to it. So, we can have the full lineage of the use case and also be able, as Maurice said before, to be sure and also make modifications in case some parts of the lineage are affected by an outage or a problem and go back to the use case affected. So, moving into the enterprise development cycle, so we talked a lot about metadata and registries, but how do actually Amplifone developers develop MCP servers and agent to agent servers to deploy them in production? We deployed and developed two repositories, one for MCP and one for agent to agent protocol. These two repositories are template repositories on GitHub. So, then developers and teams can start from from them and work their way up to production environment. The idea is that these two blueprints actually have boilerplate provided and also infrastructure and tooling already present. So, for instance, Docker files, package manager, both are fast API servers, so they are exposed in the same way. And also the authentication and cost tracking cost tracking is handled inside the blueprint. We also have an integration to LongFuse, which is an observability tool that we deployed at the platform level. So, the development teams can also trace their agents, run evaluations, and check how the agent is performing. Also, the eight way server blueprint is agnostic, so it's not based on a particular framework, so LongChain or Agno or any other framework, but actually is composed of interfaces and ports so that every team can implement their own solution in their framework of choice. The important thing is that they provide the same interface that we saved in the in the blueprint. So, that uh the development is uh easy on the developers and they can focus on the actual value of the agent. So um Mauro talked about the um CICD uh that is in place in the um uh A2A uh blueprint and also the MCP blueprint. Uh the idea is that uh once you are ready with your development, uh you can tag um a certain branch and uh GitHub action so uh starts and uh uh all not only publishes the Docker image on our artifact repository, uh but also publishes the metadata of that agent so the agent card for uh agent-to-agent protocol and the server.json uh for MCP uh onto the uh backend uh let's say proxy of the the catalog of the registries. Um in this image we can also see that um in case any AI agents uh needs to call either an MCP or an A2A proxy, um the idea is that they can go through the um APG AI gateway that we deployed and um these two proxies, so the MCP one and the uh A2A one, um go uh look up into the uh actual catalog of uh agents and MCPs to retrieve the actual URL of the backend that the agent uh wants to call and then the agent authenticate itself uh with another header um onto the the actual server. So to bring it back to the business perspective, what we achieved with the amplify platform and the registry uh we developed, we have right now a catalog uh to to make the governance happen. So we see the MCP and A2A server that we deploy across the organization and across multiple teams. Uh we have a full traceability of the use cases, agents, tools, and also models adopted um across across the use cases. Then we have the production ready blueprints for developers to start from something standard across across teams, but uh ready for building and focusing on um the business logic in the use cases. Then we have the CICD pipelines that ties for deploying the servers to production, but also the metadata into the registry. Of course, it is still in progress the the work on this platform, so we are keep growing the capabilities. Uh so feel free to to reach out to us and keep in touch if you have any similar uh point of view or also uh something different that you want to discuss, more than welcome. Thank you. Feel free to reach out.