Building AI Agents That Actually Work in Production

The Gap Between Demo and Production

Every week there's a new AI agent demo that looks incredible. An agent that books flights, writes code, manages your calendar — all from a single prompt. The demos are impressive. The production reality is... different.

At Infinitiv, we've built AI agent systems for several enterprise clients. Here's what we've learned about making them actually reliable.

The Reliability Problem

AI agents fail in ways that traditional software doesn't. A REST API either returns the right data or throws an error. An AI agent might confidently return wrong data, take an unexpected action, or get stuck in a loop — all while appearing to work perfectly.

The fundamental challenge is that LLMs are probabilistic, but business logic needs to be deterministic. Bridging this gap is where the real engineering happens.

Patterns That Work

Constrained Action Spaces

Don't give your agent access to everything. Define a strict set of tools and actions, with clear input/output schemas. An agent that can do 5 things well is infinitely more useful than one that can attempt 50 things poorly.

Structured Output Validation

Every agent response should be validated against a schema before any action is taken. We use Zod schemas that match our tool definitions, so malformed agent outputs are caught immediately rather than corrupting downstream data.

Human-in-the-Loop for High-Stakes Actions

For actions that can't be easily reversed — sending emails, modifying financial records, deleting data — always require human confirmation. The agent can prepare the action, but a human approves it.

Retry Logic With Guardrails

Agents will sometimes fail on their first attempt. Simple retry logic helps, but you need guardrails: maximum retry counts, exponential backoff, and circuit breakers that escalate to human operators when the agent is clearly stuck.

Memory and Context Management

Long-running agents accumulate context that can drift or become contradictory. We implement explicit memory management: summarizing long conversations, pruning irrelevant context, and maintaining a structured "state of the world" that the agent references alongside its conversation history.

Monitoring AI Agents

Traditional application monitoring isn't enough. You need to track:

• Task completion rates (did the agent actually accomplish what was asked?)

• Action accuracy (did it take the right actions?)

• Hallucination detection (did it reference things that don't exist?)

• Cost per task (LLM API calls add up fast)

• Latency distribution (agents can have highly variable response times)

We built custom dashboards that surface these metrics in real-time, with alerts that trigger when accuracy drops below our defined thresholds.

The Architecture

Our standard agent architecture looks like this:

• An orchestrator layer that manages the conversation loop

• A tool registry with typed interfaces for each available action

• A validation layer that checks every agent output before execution

• An audit log that records every decision and action for debugging

• A fallback system that gracefully degrades to simpler automation when the agent can't handle a request

When NOT to Use Agents

Not every automation needs an AI agent. If the workflow is well-defined with clear branching logic, a traditional state machine or workflow engine is more reliable, cheaper, and easier to debug.

Use AI agents when the input is ambiguous, the decision space is large, or the task requires understanding natural language context. Use traditional automation for everything else.

The hype around AI agents is warranted — they can genuinely transform enterprise workflows. But only if you engineer them with the same rigor you'd apply to any production system.