AI Agent Orchestration
Build reliable multi-step AI agents with tool use, planning loops, human-in-the-loop checkpoints, error recovery, and observability.
AI agents are LLM-powered systems that autonomously plan and execute multi-step tasks using tools. Beyond simple Q&A, agents browse the web, execute code, call APIs, and coordinate work across multiple steps to accomplish complex goals.
Start simple: a single LLM call is better than a complex agent if it solves the problem. Add agents when the problem genuinely requires multi-step reasoning or tool use. Build checkpoints-humans should be in the loop for high-stakes or irreversible actions.
Workflow
- Define tools with precise schemasVague tool descriptions cause the LLM to misuse tools or hallucinate parameters.
- Implement the ReAct loopReAct (Reasoning + Acting) interleaves thinking and tool use - producing more reliable, auditable agent behavior than pure action loops.
- Add human-in-the-loop checkpointsAgents can make irreversible mistakes (deleting files, sending emails, billing customers) - humans must approve high-risk actions.
- Handle errors and failures gracefullyAgents operating over multiple steps will encounter tool failures; how they recover determines if they complete or loop endlessly.
- Add observability and evaluationMulti-step agents are difficult to debug without visibility into each step; observability is essential for reliability.
Output Format
Typed tool definitions, ReAct loop implementation, risk-classified tools with human approval flow, error recovery logic, LangSmith or equivalent tracing.
Examples
Tool definition: `{ name: "send_email", description: "Send an email to a user. HIGH RISK: requires human approval before execution.", parameters: { to: { type: "string" }, subject: { type: "string" }, body: { type: "string" } } }`ReAct step: `[THOUGHT] I need current pricing. I should search the web.
[ACTION] search_web({ query: "iPhone 16 Pro price 2026" })
[OBSERVATION] iPhone 16 Pro starts at $999...
[THOUGHT] I have the information needed. I can now answer.`Error handling: `try { const result = await tool.execute(params); return { success: true, result }; } catch (e) { return { success: false, error: e.message, suggestion: "Try alternative tool or retry with different params" }; }`Edge Cases & Gotchas
- Infinite loops: agents can get stuck in tool-retry loops; add global step counter and hard limit; detect repeated identical tool calls and break the loop
- Context window overflow: long agent runs exceed context limits; summarize earlier steps periodically and maintain a working memory of key facts
- Tool side effects in planning: agents may call state-changing tools during "planning" phase; mark planning-only tools with read-only flag and separate from execution tools
When to Break These Rules
- For deterministic workflows with known steps, use a traditional pipeline (hardcoded orchestration) instead of an agent. Agents add non-determinism and cost - use them only when planning must adapt to dynamic inputs.
Reference Files
Anthropic agent patterns: https://docs.anthropic.com/claude/docs/tool-useLangGraph: https://langchain-ai.github.io/langgraph/LangSmith: https://docs.smith.langchain.com/ReAct paper: https://arxiv.org/abs/2210.03629