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Agent Patterns
Patterns that didnβt break production (with code and trade-offs).
Not sure which pattern you need? Design your agent β
- ReAct Agentβ β βMaster the ReAct agent pattern in React: reason-act loops, tool use, and guardrails that prevent common failures in production AI workflows.
- Task Decomposition Agentβ β βDecompose complex goals into manageable subtasks so agents can plan, execute, and verify step by step with better reliability.
- Routing Agentβ β βRoute each request to the best agent or tool using explicit criteria, improving accuracy, speed, and cost efficiency in multi-agent systems.
- Orchestrator Agentβ β βLearn orchestration patterns to delegate subtasks, run executors in parallel, track status, and merge outputs into one reliable final result.
- Supervisor Agentβ β βUse a supervisor agent to validate proposed actions, enforce policies, and block unsafe steps before they impact users or systems.
- Multi-Agent Collaborationβ β βUse collaborating agents to split roles, exchange intermediate results, and cross-check outputs to solve complex tasks with higher quality.
- RAG Agentβ β βBuild a RAG agent that finds relevant documents, cites sources, and reduces hallucinations in answers.
- Memory-Augmented Agentβ β βBuild agents that remember user facts and prior outcomes across sessions to deliver consistent, personalized responses without losing control.
- Reflection Agentβ β βAdd one short review pass to catch obvious mistakes before answering, without endless rewrites.
- Self-Critique Agentβ β βRun a safe self-critique loop: one schema-based review, one constrained revision, and a change log for stable quality.
- Fallback Recovery Agentβ β βBuild an agent that recovers from tool and model failures with fallback strategies, retries, and controlled degradation.
- Guarded Policy Agentβ β βImplement a policy-gate that allows, denies, rewrites, or escalates risky agent actions for safe and auditable execution.
- Code Execution Agentβ β βHow an agent runs code in a sandbox to compute reliably, validate hypotheses, and automate tasks with production guardrails.
- Data Analysis Agentβ β βHow an agent ingests, cleans, analyzes, and validates data to produce reproducible metrics and conclusions for decisions.
- Research Agentβ β βUse a bounded research pipeline: search, read, extract facts, and synthesize with citations without tool spam or infinite loops.