How to Make AI Agents Reliable in Production

Step 1: Build the Observability Layer

Observability for agents means capturing every model call, every tool invocation, every reasoning trace, and every decision point, so that when something goes wrong you can reconstruct exactly what happened and why. This is more detailed than standard application logging because agent behavior is emergent: the sequence of actions is not predetermined, so you cannot know in advance which steps will be relevant to a failure investigation.

The minimum logging for each agent task includes: the full system prompt, the user's input, each model response (including reasoning text and tool calls), each tool call with its parameters and result, the total number of iterations, the total token count and estimated cost, the final output, and whether the task succeeded or failed. Structure this as a trace, a sequential log of events with timestamps, so you can follow the agent's decision path from start to finish. LangSmith provides this out of the box for LangGraph-based agents, and similar tools exist for other frameworks.

Beyond logging, build alerts for anomalous behavior: tasks that exceed the expected iteration count (the agent might be looping), tasks that exceed the expected cost (the context window might be growing uncontrollably), tasks where the agent calls the same tool repeatedly with the same parameters (a stuck loop), and tasks where guardrails are triggered (the agent attempted something it should not). These alerts catch problems in real time rather than during post-mortem reviews.

Step 2: Implement Error Recovery

Agents encounter errors at three levels: tool errors (an API call fails, a database query returns an error, a timeout occurs), model errors (the API rate-limits you, the model returns an invalid response, or the model refuses to answer), and reasoning errors (the agent takes a wrong path and gets stuck). Each level requires different recovery strategies.

For tool errors, implement retries with exponential backoff for transient failures (network timeouts, rate limits), clear error messages for permanent failures (invalid parameters, missing permissions), and fallback tools when available (if one search API is down, try another). The key design principle is that tool errors should be returned to the agent as informative messages, not thrown as exceptions that crash the loop. An agent that receives "Error: the API returned 429 Too Many Requests, please wait and retry" can wait and retry on its own. An agent whose loop crashes on an unhandled exception cannot.

For model errors, implement a retry with a slightly modified prompt (rephrasing the question or adding clarification), a fallback to a different model (if the primary model is down or rate-limited, try a secondary), and a maximum retry count after which the agent reports the failure gracefully. Model refusals (the model declines to answer because it detects a policy violation) should be logged and investigated, because they may indicate a legitimate safety concern or a false positive that needs a prompt adjustment.

For reasoning errors, the primary tool is the iteration limit combined with stuck-loop detection. If the agent calls the same tool with the same parameters three times in a row, or if it has used half its iteration budget without making progress (as measured by new information gathered or sub-tasks completed), inject a nudge into the context: "You appear to be stuck. Summarize what you have found so far and try a different approach." This gives the model a chance to self-correct before the hard limit kicks in.

Step 3: Manage State and Checkpoints

Long-running agent tasks are vulnerable to interruption: the process crashes, the server scales down, the user disconnects, or a deployment rolls out. Without state management, any interruption means the task starts from scratch, wasting the work already done and frustrating the user. Checkpointing solves this by persisting the agent's state at regular intervals so it can resume from the last checkpoint.

A checkpoint captures the complete state needed to resume: the conversation history, the current iteration count, any intermediate results stored in working memory, the current plan (if the agent uses plan-then-execute), and the status of each sub-task (for multi-agent systems). LangGraph has built-in checkpointing that persists state to a database after each node execution, so resuming is automatic. For custom agent implementations, checkpoint after each complete iteration of the loop: model call, tool execution, result integration.

Checkpointing also enables durability for tasks that span multiple sessions. A research agent that gathers data over several hours can checkpoint its progress so that if it is interrupted, it resumes from where it left off rather than re-doing all the searches. This connects directly to the durable execution patterns for agents, where task state is persisted reliably and the agent can survive any failure without losing work.

Step 4: Build Regression Tests

Agent systems change frequently: the system prompt gets updated, tools are added or modified, model versions change, and guardrails are adjusted. Each change can affect agent behavior in unexpected ways. Regression tests catch quality drops before they reach users by running the agent against a fixed set of test cases after every change and comparing results to a baseline.

A production agent regression suite should include 50 to 100 test cases covering: common tasks (the normal cases that represent the bulk of production traffic), edge cases (unusual inputs, ambiguous requests, incomplete information), failure cases (tool errors, missing data, contradictory information), and adversarial cases (prompt injection attempts, out-of-scope requests, attempts to bypass guardrails). For each test case, define the expected outcome (the correct answer, the acceptable range of actions, the guardrails that should or should not trigger).

Because agent behavior is non-deterministic, run each test case multiple times (3 to 5 runs) and track the success rate rather than expecting a single correct answer. A test case that succeeds 5 out of 5 times is reliable. One that succeeds 3 out of 5 times has a reliability problem that needs investigation. Track these metrics over time: a test case that was 100% reliable last week and is 60% reliable this week signals a regression, even if the average across all test cases looks fine. The agent evaluation guide covers testing methodology in depth.

Step 5: Design Graceful Degradation

Not every agent task can be completed successfully. Tools go down, information is not available, the task is more complex than the agent can handle, or the iteration limit is reached before the task is done. The question is not whether these failures will happen, but what the agent does when they happen. Graceful degradation means the agent produces the best possible result given its constraints, rather than returning an error or a meaningless output.

The principle is: partial results with transparency are better than complete failure. An agent that gathers three out of five requested data points and reports "I found pricing data for three of the five competitors. I was unable to access data for Acme Corp (their website returned an error) and Beta Inc (no pricing information found on their public site)" is far more useful than one that reports "Error: task could not be completed" or, worse, one that silently hallucinates the missing data to produce a complete-looking result.

Implement graceful degradation at two levels. At the iteration level, when the agent hits its limit, prompt it to summarize what it has accomplished and what remains. At the tool level, when a critical tool fails, have the agent acknowledge the gap and proceed with what is available, flagging the gap in its output. Both of these require explicit instructions in the system prompt: "If you cannot complete the full task, provide what you have found so far, clearly state what you were unable to accomplish, and explain why." Without this instruction, the model may try to power through failures by guessing, which produces confident-sounding but wrong outputs.