Architect a production-grade deep-research agent from first principles —owning the agent loop, tool surface, retrieval strategy, and verification layer yourself. Each layer is motivated by watching the simpler version fail, so you gain the architectural judgment to design agentic systems for your own problems.
Advanced Architecture Workshop
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What you´ll learn |
Agent loop and a disciplined tool surface; query decomposition, planning, and query generation/rewriting
Retrieval strategy: search-API trade-offs (keyword vs. semantic vs. freshness), reranking, and the live-fetch-vs-pre-crawl decision
Context engineering and evidence memory under a token budget; single-agent vs. orchestrator-worker multi-agent designs
Grounding and citation discipline (“no citation, no claim”); anti-hallucination: claim decomposition, NLI checks, runtime judge,self-repair
Eval-driven development and production hardening: LLM-as-judge, faithfulness metrics, tracing, cost/latency budgets, prompt-injection guardrails
Pick the path that matches your team – Read more
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Target audience |
Mid-to-Senior Software Engineers —New to Agents
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Prerequisites |
Comfort with Python and APIs. No prior LLM-engineering experience required.
