Building AI Agents That Actually Work in Production

Demos Are Easy. Production Is Where It Gets Real

I've helped build AI agents that run in production, handling real user requests at scale. Before that, I watched a dozen impressive demos fall apart the first time something unexpected happened. The difference between an agent that works in a demo and one that works reliably in production is not model capability — it's how you handle the inevitable moment when the model is wrong, the tool call fails, or the user asks for something you didn't design for.

The past year saw a wave of agentic announcements. Companies showing slick two-minute demos of an AI agent booking flights, writing code, managing email. What those demos almost never show is the error state. What happens when the flight API is down? What happens when the model hallucinates a function call? What happens when the user says something the designer never anticipated? that's where agents live or die.

Design for Failure From the Beginning

The single most important mindset shift: your agent will fail regularly, in ways you didn't anticipate. Design for graceful degradation from day one, not as an afterthought. Every tool call should have a fallback. Every error should be caught and handled explicitly. When the web search fails, the agent should say it couldn't retrieve that right now — not crash the session or silently return wrong information.

This sounds obvious when you say it out loud. In practice I've seen production agents deployed with zero error handling on tool calls because the demo environment never triggered an error. The first time a real user hit an edge case, the agent would spin into a retry loop and burn through tokens until it hit a timeout.

The most common failure pattern in production agent deployments is underestimating the rate at which individual tool calls fail. In a pipeline with 10 sequential tool calls each succeeding 95% of the time, the overall success rate is only 60%. Production agents must be designed with explicit retry logic, fallback strategies, and graceful degradation paths — not added as afterthoughts when incidents occur.

Circuit breakers are essential at the tool level: if an external API is returning errors, the agent should detect this and route around it rather than hammering a degraded service. Timeout budgets should be allocated per-task, not per-tool-call, so an agent that is running slow can still complete within SLA by simplifying its approach rather than failing entirely. These patterns are standard in distributed systems engineering and apply equally to agentic AI deployments.

Observability Is Not Optional

Log everything: every tool call, every LLM inference, every state transition, the input and output of each step. Not for debugging after something goes wrong — for understanding what your agent is actually doing before something goes wrong. Teams that skip this ship agents that work in staging and mysteriously misbehave in production, with no idea why because they have no visibility.

The tooling has improved considerably. LangSmith, Langfuse, and Weights and Biases all have decent agent tracing now. Pick one and integrate it before you write a single line of agent logic. Retrofitting observability into an existing agent is painful. Starting with it costs almost nothing. What to log at minimum: the full prompt sent to the model, the raw response, which tool was selected, the tool input and output, any retry attempts, and total latency per step. [OpenTelemetry documentation]

You cannot debug an agent you cannot observe. Production agent systems require structured logging of every tool call, every LLM invocation, every decision point, and every error — with full input/output captured for post-hoc analysis. Trace IDs that propagate through multi-step agent runs are critical for diagnosing failures that occur several steps removed from their root cause.

Beyond basic logging, evaluation pipelines that continuously run agents against a golden set of test cases are the difference between reliable and unreliable production systems. Every time an agent fails in production, that failure case should be added to the evaluation suite before the fix is deployed. This discipline — borrowed from traditional software testing — is what separates teams shipping AI agents that improve over time from teams perpetually firefighting.

State Management at Scale

Most agent frameworks handle toy examples fine. Conversation history fits in memory, tool results are small, the whole thing runs in one process. Real production agents have different constraints: sessions that span hours, tool results that are hundreds of kilobytes, multiple concurrent users sharing infrastructure, and recovery requirements when a process crashes mid-task.

The pattern that works: treat agent state as a first-class data structure that gets persisted to a database at each step. don't keep it only in memory. If the process dies, you can resume from the last checkpoint. If you need to debug, you can inspect the full state at any point in the execution history. Redis with a reasonable TTL is a simple and effective starting point for most teams.

The Context Window Is Your Budget

Every token in your context window is a cost — not just monetary, but latency cost and the model's attention diluted across more content. this is the reason your agent gets confused when conversations run long. Managing context is one of the highest-use engineering decisions in a production agent.

Strategies that work: summarise completed sub-tasks instead of keeping full tool outputs in context, implement a sliding window for conversation history, and use retrieval to pull in relevant context on demand rather than stuffing everything upfront. One team I worked with cut their per-query cost by 60 percent simply by implementing a context compression step that ran after each tool use.

Tool Design Determines Agent Quality

The model is only as good as the tools you give it. Poorly designed tools — ambiguous descriptions, inconsistent parameter naming, no validation, missing error messages — produce unpredictable agent behaviour that's nearly impossible to debug. Well-designed tools make the agent look smarter than it's.

Every tool description should explain what the tool does, what it returns, and what it doesn't do. Parameter names should be self-explanatory without reading the description. Tools should validate their inputs and return structured errors, not exceptions. And each tool should do exactly one thing — resist the temptation to build Swiss Army knife tools that handle five variations of a task.

Evaluation: The Part Everyone Skips

You need an eval suite before you ship and a regression test suite that runs on every deploy. With agents the stakes are higher than with simple LLM calls — a broken agent doesn't just return a bad answer, it can take a series of harmful actions or get stuck in a loop that costs real money.

Minimum viable eval: a golden set of 50 to 100 test cases with known correct outcomes, automated scoring on the final state, a latency budget that triggers a warning if exceeded, and cost-per-task tracking. Run it on every deploy. If you can't tell whether a change made your agent better or worse, you're flying blind.

When to Not Use an Agent

After all of this, the most important production lesson: agents are often overkill. A chain of deterministic steps with a single LLM call for the language task is usually cheaper, faster, more reliable, and easier to debug than a full autonomous agent. Use agents when you genuinely need dynamic tool selection across unpredictable tasks. For everything else, the simpler architecture will serve your users better and your team better when something breaks at 2am.