Agents in Production

An AI agent is a model that uses tools in a loop: you give it a goal, it decides what to do next, calls a tool, looks at the result, and keeps going until the task is done. That loop is the core idea—everything else is built on top of it. A simple way to picture an agent is as a folder with three things inside: instructions (a markdown file), skills (packaged capabilities), and a connection to data or tools. You don’t always build the engine from scratch; you take a general-purpose agent and put skills on top of it, like standard operating procedures you already have written down.

Getting an agent from demo to production matters because it means solving four problems at once: customization (connecting to real data with security and compliance), evaluation (measuring quality before users see it), deployment (scalable infrastructure with CI/CD), and observability (monitoring in real time). Most teams get stuck between evaluation and deployment—the agent works on a laptop but fails in production, and that gap takes 3 to 9 months to close.

Why does this matter for AI development? Because we’re moving from building separate agents for each use case to designing what agents should do, where they should be proactive, and how they work together. The skill isn’t coding; it’s designing workflows and giving agents the building blocks—skills, plugins, and procedures—they need to act reliably. As models get faster and cheaper, agents will drive more actions, so getting them to production safely and observably is the real competitive edge.

To use agents (AI programs that complete tasks on their own) in production, start by defining what each agent should do and when to use it. Create a new agent by selecting whether it’s personal, global, or project-specific. Then, either generate its configuration with Claude or manually set it up. Describe the agent’s purpose clearly so it knows its parameters.

Once built, an agent is just an LLM (a language model) that runs tools in a loop—it has goals, context, and an environment to act in. Give it a workflow (a sequence of steps) as guidelines; for example, gather data from five competitor sources, analyze findings, analyze your business, and create a PDF report. As it works, you can steer it by saying “I liked this, change that,” refining outputs in real-time.

For production use, run multiple agents in separate panes or instances. In one pane, tell an agent what to do quickly, then move to another, cycling through to validate outputs and adjust direction. This lets you scale horizontally (add more agents as needed) and isolate them to avoid interference.

Start with a pilot where you manually do the work with AI, then productize (turn it into a repeatable system). For example, build an agent to continuously optimize production app performance—it analyzes issues and helps fix them. The goal is to empower engineers, not replace them, so design the product around user feedback and trust by being transparent about what agents do.

Agents in production fail in predictable ways. The most dangerous failure is not a bad output—that’s easy to spot and fix. The real risk is a polished artifact that looks correct at a glance but will never pass your exit gates. This cascading failure happens when an agent autonomously produces something plausible, like an investment case built on irrelevant facts, and it gets forwarded without scrutiny.

The root cause is often overcomplication. Every single thing you add to an agent risks making it worse. Large system prompts and lots of edge cases (unusual scenarios) crowd the model’s attention, making it dumber, not smarter. Instead, treat agents as models using tools in a loop—give them clear guidelines, not exhaustive rules.

Because agents are non-deterministic (unpredictable in behavior), you can’t know if a task fails due to a bad skill or because it’s too hard for the model. So never ship skills without evals (tests that measure agent performance). Add stop conditions and validations to catch faults, and design your attention like a system—decide where you enter, what you require, and what you reuse.

In production, monitor traces (logs of agent actions) and feed them back into an optimization loop, just like CI/CD, but don’t rebuild a worse version of it manually. Also, balance capability with safety: give agents enough power to act, but not so much they do something unsafe. Finally, remember demos work because data is clean; production bugs are high because agents face unseen future data, so set up guards as you onboard real users.