Your agent remembers, orchestrates, and shares state. The last move is the one you have been building toward — an agent that critiques and improves its own output without you in the loop.
So the final week is the self-correcting agent? One pass writes, a second pass scores, a third pass rewrites if the score is low?
That is exactly the shape. Five patterns: retrieval-augmented answers, Pydantic evaluation with a score, a threshold-driven refinement loop, a multi-field plan with steps and priority, and a capstone that chains search, plan, and stateful reply into one function.
And the capstone is the multi-turn assistant — everything from Week 1 through Week 4 in one function?
Everything. Search for context, plan typed steps, track history, return the full routine state. By Friday your agent runs itself, scores its own answers, and improves until it meets the bar you set.
rag_answer: search() results injected as context into an agent callevaluate_response: Pydantic Score(score, feedback) from a critic agentrefine_with_feedback: loop until score crosses a thresholdbuild_agent_plan: multi-field Plan(steps, priority, estimated_actions)full_agent_run: capstone multi-turn assistant chaining search, plan, and historyGoal: by Friday the agent is autonomous — retrieves, plans, improves, and remembers.
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Your agent remembers, orchestrates, and shares state. The last move is the one you have been building toward — an agent that critiques and improves its own output without you in the loop.
So the final week is the self-correcting agent? One pass writes, a second pass scores, a third pass rewrites if the score is low?
That is exactly the shape. Five patterns: retrieval-augmented answers, Pydantic evaluation with a score, a threshold-driven refinement loop, a multi-field plan with steps and priority, and a capstone that chains search, plan, and stateful reply into one function.
And the capstone is the multi-turn assistant — everything from Week 1 through Week 4 in one function?
Everything. Search for context, plan typed steps, track history, return the full routine state. By Friday your agent runs itself, scores its own answers, and improves until it meets the bar you set.
rag_answer: search() results injected as context into an agent callevaluate_response: Pydantic Score(score, feedback) from a critic agentrefine_with_feedback: loop until score crosses a thresholdbuild_agent_plan: multi-field Plan(steps, priority, estimated_actions)full_agent_run: capstone multi-turn assistant chaining search, plan, and historyGoal: by Friday the agent is autonomous — retrieves, plans, improves, and remembers.