Tool-Using Agents in Production: Reliability Patterns That Actually Work
Agents are easy to demo and hard to operate. Here’s a reliability-focused blueprint: constrained actions, observable state, evaluation gates, and safe fallbacks.
The agent reliability gap
An agent pipeline combines planning, tool execution, and summarization. Each stage can fail silently: a slightly wrong plan, a tool called with the wrong arguments, or a correct result summarized incorrectly.
Treat agents as distributed systems: they need timeouts, retries, structured logs, and strong contracts between stages.
Constrain actions with schemas and allow-lists
Define tool schemas that are strict and minimal. Validate every argument server-side and reject unexpected fields. An allow-list of permissible operations (and resources) prevents the agent from exploring dangerous or irrelevant actions.
If a tool can mutate state (delete, purchase, send), require an explicit confirmation step and bind the confirmation to a specific tool call payload.
Make state observable and replayable
Store a structured execution trace: inputs, tool calls, tool outputs, and intermediate decisions. This enables replay debugging and offline evaluation.
Prefer deterministic tools and version them. If a tool output changes over time (like a search API), attach timestamps and cache keys so you can reproduce outcomes.
Evaluation gates before the final answer
Add lightweight checks: did the agent cite the right sources? did it call a required tool? did it produce forbidden content? did it stay within policy?
Even a simple rules engine (e.g., “must include order id”, “must not expose secrets”) catches a surprising number of failures.
Fallback behavior is a feature
When uncertain, a robust agent asks a clarifying question or returns a safe partial result. Users prefer honest limitations over confident errors.
Design your UX for uncertainty: show steps completed, show what’s missing, and provide a “try again” path.