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What we build:
A production-ready Autonomous Research Agent that answers multi-hop questions about AI safety using a seeded ChromaDB corpus
An internal validation loop: every retrieval batch is scored before the Synthesizer sees it, triggering automatic re-retrieval when quality gates fail
A traceable execution graph persisted to SQLite, visible in a React timeline dashboard
A Ragas evaluation pipeline run automatically post-answer, writing per-question metric reports
An evaluation-gated deployment flag: the agent marks itself “production-ready” only when all four reliability thresholds pass
Connection to L44: L44 gave us a standalone
RagasEvaluatorandEvaluationGatecapable of scoring any{question, answer, contexts}triple against faithfulness ≥0.85, answer_relevancy ≥0.80, context_recall ≥0.75, and context_precision ≥0.70. L45 embeds those components directly into the agent’s execution path — evaluation is no longer a post-hoc audit; it is a first-class runtime gate.
Enables L46: By the end of this lesson you have a working multi-agent pipeline (Planner → Retriever → Validator → Synthesizer) where each agent is a discrete, message-passing unit with defined input/output schemas. L46 extends this exact architecture into full Multi-Agent System (MAS) theory — adding specialist sub-agents, broadcast coordination, and shared blackboard state.
Architecture Context
L45 sits at the apex of Module 4 (RAG Implementation). The module arc was:
L40–41: Vector store fundamentals and hybrid search
L42: Distributed trace propagation (W3C TraceContext)
L43: Four-agent Agentic RAG pipeline (Planner, Retriever, Validator, Synthesizer)
L44: Ragas evaluation framework wired to Gemini 1.5 Pro
L45 (now): Integration capstone — all prior components converge into a single deployable system
The L43 pipeline becomes the agent runtime. The L44 evaluator becomes the reliability enforcement layer. The L42 tracer becomes the observability backbone. Nothing is rebuilt from scratch; everything is composed.
