--- schema: 1 type: video title: >- Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS description: >- AI agents that book 15 guests in a 10-person room. Agents that fabricate statistics when data doesn't exist. Agents that pick wrong tools from 29 options, wasting $47 in tokens. These aren't prompt engineering failures, they're architectural limitations that need structural solutions. This hands-on workshop covers 5 research-backed techniques to prevent agent hallucinations: 1. Graph-RAG (Neo4j) - Replace vector similarity guessing with precise entity relationships. Result: 73% fewer fabricated statistics. 2. Semantic Tool Selection - Filter 29 tools to the relevant 5 using embeddings. Result: 89% token reduction, accurate tool selection. 3. Multi-Agent Validation - Executor-Validator-Critic swarms catch fabrications through cross-checking. Result: 92% detection rate. 4. Neurosymbolic Guardrails - Framework-enforced rules (lifecycle hooks) that agents cannot bypass. Result: Zero business rule violations. 5. Agent Steering - Guide agents to self-correct instead of blocking them. Result: Task completion without hard failures. Each demo includes live code, before/after metrics, and failure case analysis. Final module shows production deployment. You'll walk away with working Python implementations, a decision framework for when to apply each technique, and an open-source repository adaptable to your domain. code: https://github.com/elizabethfuentes12/why-agents-fail-sample-for-amazon-agentcore Speakers: - Elizabeth Fuentes (AWS): Elizabeth Fuentes is a developer advocate and AI engineer focused on what makes agents fast, cheap, and correct in production. She turns failure modes (hallucination, token blowups, context overflow, lost memory) into named, measurable fixes, each backed by a runnable demo and before/after numbers. Her work covers the architectural decisions behind reliable agents: context offloading, the split between conversation and data memory, semantic versus exact-reference retrieval, guardrails, and agent evaluation. With 107+ published technical articles and a Master's in Data Science, she shares production agent patterns across English and Spanish developer communities, and likes turning complex concepts into something anyone can learn. X/Twitter: https://x.com/ElizabethFue12 LinkedIn: https://www.linkedin.com/in/lizfue/ GitHub: https://github.com/elizabethfuentes12 --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/vJukHCIv7Ck-stop-ai-agent-hallucinations-5-techniques-production-patterns-elizabeth-fuentes-leone-aws Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- canonical: >- https://liquidgold.cc/channel/aidotengineer/explore/video/stop-ai-agent-hallucinations-5-techniques-productio-3f98d5ca6d7a markdown: >- https://liquidgold.cc/channel/aidotengineer/video/stop-ai-agent-hallucinations-5-techniques-productio-3f98d5ca6d7a.md llms: null parent: 'https://liquidgold.cc/channel/aidotengineer.md' channel: aidotengineer updated: '2026-10-24T08:10:02.249Z' language: null children: [] videoId: vJukHCIv7Ck durationSeconds: 3319 publishedAt: '2026-07-11T21:45:00.000Z' searchChunks: - ordinal: 0 text: null startSeconds: 0 endSeconds: 11 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=0' heldReason: rights - ordinal: 1 text: null startSeconds: 11 endSeconds: 92 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=11' heldReason: rights - ordinal: 2 text: null startSeconds: 11 endSeconds: 92 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=11' heldReason: rights - ordinal: 3 text: null startSeconds: 92 endSeconds: 104 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=92' heldReason: rights - ordinal: 4 text: null startSeconds: 104 endSeconds: 189 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=104' heldReason: rights - ordinal: 5 text: null startSeconds: 104 endSeconds: 189 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=104' heldReason: rights - ordinal: 6 text: null startSeconds: 104 endSeconds: 189 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=104' heldReason: rights - ordinal: 7 text: null startSeconds: 201 endSeconds: 302 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=201' heldReason: rights - ordinal: 8 text: null startSeconds: 313 endSeconds: 395 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=313' heldReason: rights - ordinal: 9 text: null startSeconds: 532 endSeconds: 620 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=532' heldReason: rights - ordinal: 10 text: null startSeconds: 778 endSeconds: 868 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=778' heldReason: rights - ordinal: 11 text: null startSeconds: 977 endSeconds: 1079 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=977' heldReason: rights - ordinal: 12 text: null startSeconds: 1090 endSeconds: 1194 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=1090' heldReason: rights - ordinal: 13 text: null startSeconds: 1090 endSeconds: 1194 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=1090' heldReason: rights - ordinal: 14 text: null startSeconds: 1403 endSeconds: 1492 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=1403' heldReason: rights - ordinal: 15 text: null startSeconds: 1598 endSeconds: 1723 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=1598' heldReason: rights - ordinal: 16 text: null startSeconds: 1734 endSeconds: 1846 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=1734' heldReason: rights - ordinal: 17 text: null startSeconds: 1734 endSeconds: 1846 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=1734' heldReason: rights - ordinal: 18 text: null startSeconds: 2181 endSeconds: 2293 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=2181' heldReason: rights - ordinal: 19 text: null startSeconds: 2181 endSeconds: 2293 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=2181' heldReason: rights - ordinal: 20 text: null startSeconds: 2305 endSeconds: 2385 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=2305' heldReason: rights - ordinal: 21 text: null startSeconds: 2628 endSeconds: 2720 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=2628' heldReason: rights - ordinal: 22 text: null startSeconds: 2732 endSeconds: 2812 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=2732' heldReason: rights - ordinal: 23 text: null startSeconds: 2812 endSeconds: 2822 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=2812' heldReason: rights - ordinal: 24 text: null startSeconds: 3093 endSeconds: 3186 watch: 'https://liquidgold.cc/watch/vJukHCIv7Ck?t=3093' heldReason: rights --- # Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS AI agents that book 15 guests in a 10-person room. Agents that fabricate statistics when data doesn't exist. Agents that pick wrong tools from 29 options, wasting $47 in tokens. These aren't prompt engineering failures, they're architectural limitations that need structural solutions. This hands-on workshop covers 5 research-backed techniques to prevent agent hallucinations: 1. Graph-RAG (Neo4j) - Replace vector similarity guessing with precise entity relationships. Result: 73% fewer fabricated statistics. 2. Semantic Tool Selection - Filter 29 tools to the relevant 5 using embeddings. Result: 89% token reduction, accurate tool selection. 3. Multi-Agent Validation - Executor-Validator-Critic swarms catch fabrications through cross-checking. Result: 92% detection rate. 4. Neurosymbolic Guardrails - Framework-enforced rules (lifecycle hooks) that agents cannot bypass. Result: Zero business rule violations. 5. Agent Steering - Guide agents to self-correct instead of blocking them. Result: Task completion without hard failures. Each demo includes live code, before/after metrics, and failure case analysis. Final module shows production deployment. You'll walk away with working Python implementations, a decision framework for when to apply each technique, and an open-source repository adaptable to your domain. code: https://github.com/elizabethfuentes12/why-agents-fail-sample-for-amazon-agentcore Speakers: - Elizabeth Fuentes (AWS): Elizabeth Fuentes is a developer advocate and AI engineer focused on what makes agents fast, cheap, and correct in production. She turns failure modes (hallucination, token blowups, context overflow, lost memory) into named, measurable fixes, each backed by a runnable demo and before/after numbers. Her work covers the architectural decisions behind reliable agents: context offloading, the split between conversation and data memory, semantic versus exact-reference retrieval, guardrails, and agent evaluation. With 107+ published technical articles and a Master's in Data Science, she shares production agent patterns across English and Spanish developer communities, and likes turning complex concepts into something anyone can learn. X/Twitter: https://x.com/ElizabethFue12 LinkedIn: https://www.linkedin.com/in/lizfue/ GitHub: https://github.com/elizabethfuentes12 --- Watch along with the corrected transcript, summary, timestamps and resources on this talk's official AIE page: https://ai.engineer/talks/vJukHCIv7Ck-stop-ai-agent-hallucinations-5-techniques-production-patterns-elizabeth-fuentes-leone-aws Join our upcoming AI Engineer conferences (see https://ai.engineer landing page): - AIEi Shanghai, November 5–6 — the first for meeting top Chinese labs and engineers - AIE CODE in SF, November 10–12 — the top AI Coding conference returns - AIEi Sydney, December 7–8 — the first AIEi run alongside NeurIPS 2026 in Sydney --- Canonical: https://liquidgold.cc/channel/aidotengineer/explore/video/stop-ai-agent-hallucinations-5-techniques-productio-3f98d5ca6d7a · Channel: [AI Engineer](https://liquidgold.cc/channel/aidotengineer.md) > Search chunk text is held until public-source provenance is verified. ## Document chunks > Missing: no stored document chunks are available.