CloudWatch

Stop AI Agent Hallucinations: 5 Techniques + Production Patterns - Elizabeth Fuentes, AWS

Jul 11, 2026 · 55:19

Elizabeth Fuentes (AWS) presents five code-based techniques to stop AI agent hallucinations, each with measurable before/after metrics. Semantic Tool Selection filters 29 tools to the 3 most relevant per query, cutting token usage from thousands to under 300 per call. Graph-RAG replaces vector similarity with structured graph queries (using Neo4j), enabling precise aggregation and multi-hop reasoning that vanilla RAG fabricates. Multi-Agent Validation uses an Executor-Validator-Critic swarm to catch fabrications, achieving a 92% detection rate. Neurosymbolic Guardrails enforce business rules in Python hooks that the agent cannot skip, achieving zero rule violations. Agent Steering guides agents to self-correct when soft rules fire, completing tasks without hard failures—demonstrated by booking 50 guests by intelligently splitting into two rooms.

Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon

Jun 29, 2026 · 14:41

Anna Marie Benzon presents an RL-guided ETL pipeline health agent that automates failure diagnosis and remediation using deterministic anomaly detection, tabular Q-learning, and an external safety layer, achieving a 99.85% reduction in mean time to recovery (to 5.24 minutes) across 30 synthetic runs. The system detects schema drift, null-rate spikes, type changes, and runtime errors, then selects from six bounded actions (retry, coerce, rollback, quarantine, escalate, log). A key design principle separates deterministic rules for observable facts from learning for contextual action selection, with safety overrides outside the learned policy to prevent unsafe autonomy. Ablation results show the Q-learning policy matches a hand-defined deterministic policy (0% difference), while the safety override reduces non-escalation by 15.03 percentage points intentionally. The talk emphasizes that reliability comes from structured state, sensible logic, and guardrails, not from RL alone, and that escalation is a capability, not a failure. Future work involves shadow-mode deployment on production incident traces.

Feedback Loops are All You Need — Mehedi Hassan, Granola

May 10, 2026 · 10:11

Mehedi Hassan, a product engineer at Granola, argues that shipping AI features into production requires building feedback loops rather than one-shotting better prompts. Granola's chat feature for meeting notes revealed problems with web search — token costs ballooning to 10p per chat, overnight provider updates silently degrading results — and with prompt personalization, as a single prompt cannot serve salespeople, engineers, and HR managers equally. To close the gap, Granola built custom internal tracing exposing tool calls, search trails, reasoning, and cost in a UI accessible to non-engineers, not just CloudWatch queries. They also refactored their Electron app's renderer to run as a web app, enabling preview links on every PR and allowing Cursor to automatically test changes and upload screenshots. The result is faster iteration and confidence that shipped features actually work for customers.

Supercharging developer workflow with Amazon Q Developer - Vikash Agrawal

Jun 10, 2025 · 13:22

Vikash Agrawal and Linda show how Amazon Q Developer can drive every stage of the software development lifecycle. They build a 2048 game with FastAPI and Poetry using Q's /dev command in the CLI, then add unit tests with /test, generate documentation with /doc, and integrate with GitHub for automated pull requests. After deploying to AWS, they use Q in CloudWatch to debug a runtime import error by analyzing logs and resource topology. The episode emphasizes that planning infrastructure and prompts upfront, along with security scans, are critical for production-ready AI-assisted development.