Building LLM Agents That Don't Hallucinate in Production

Every AI agent system we've deployed has gone through the same arc: impressive demo, confident launch, and then the edge cases start arriving. The LLM calls the wrong tool. It loops. It generates plausible-looking output that's factually wrong. The problem isn't the LLM — it's the architecture around it. Reliable agents require as much engineering discipline as any other production system.

Tool Design: The Biggest Lever on Reliability

Most agent failures trace back to tool design problems, not model problems. LLMs call tools in ways the developer didn't anticipate — with unexpected argument combinations, out-of-range values, or in the wrong sequence. The fix is defensive tool design, not model tuning.

• Validate every argument at the tool boundary: Treat tool calls like untrusted API inputs. Validate types, ranges, and allowed values before executing. Return structured errors that the agent can reason about.
• Make tools idempotent: Agents retry on failure. Non-idempotent tools (charge payment, send email, delete record) called twice will cause real problems. Use idempotency keys or check-then-act patterns.
• Narrow tool scope: A tool that does 'everything related to users' is an invitation for unintended side effects. Split into read tools (getUserById, listUsers) and write tools (updateUserEmail, deactivateUser) with explicit separation.
• Return structured results, not prose: Return { success: true, userId: '123', changes: ['email'] } not 'I successfully updated the user's email address to...' Structured results let you validate outcomes programmatically.

Guardrails That Actually Work

• Human-in-the-loop for destructive actions: Any action that's hard to reverse — send email to 1000 users, delete records, process payments — requires human approval before execution.
• Hard iteration limits: Agents can loop indefinitely if given the wrong goal or tools. Hard stop at N iterations and surface the state to a human.
• Structured output validation: Use Zod or JSON Schema to validate every tool call output before passing it to the next step. Parse, don't trust.
• Audit logs for every action: Maintain an immutable audit log of every tool call, its arguments, and its result. When something goes wrong, you need to reconstruct the full agent trajectory.