Building AI agents is not the same as building software. The mental model most engineers bring — deterministic input, predictable output — breaks down the moment you introduce a language model in the loop. Here's what to keep in mind before you write a single line of agent code.

Start with the outcome, not the tool

The most common mistake in agent building is starting with the technology. The better question is: what outcome do I want, and what's the minimum agentic loop needed to deliver it? Agents scoped to a single, clear outcome are vastly more reliable than agents given broad mandates. Narrow the scope. Define success explicitly.

Treat every tool call as a contract

When your agent calls an external tool — a CRM, a search API, a database — it's entering a contract. The tool will return data in a specific shape, and your agent needs to handle every variation: success, failure, empty results, unexpected formats. Most agent failures happen not because the model reasoned badly, but because an edge case in a tool call wasn't handled. Define your tool contracts tightly. Fail loudly, not silently.

Build for observability from day one

An agent you can't observe is an agent you can't improve. Before you optimise for performance, make sure you can see exactly what your agent is doing at every step — what it received, what it decided, what it called, what it returned. Observability is not a later concern. It is the foundation.

Keep humans in the loop at the right moments

The goal isn't to remove humans — it's to remove unnecessary human effort. Define the decision points where a human must be involved. Build explicit checkpoints. An agent that knows when to escalate is more valuable than one that never stops.

Iterate on the prompt before you iterate on the architecture

When an agent underperforms, the instinct is to add more steps, more tools, more complexity. Usually the answer is simpler: the system prompt is ambiguous, or the agent lacks context. Exhaust prompt improvements before architectural changes. The model is more capable than you think — it often just needs clearer instructions.

Agent building rewards patience, precision, and a bias toward simplicity. Start small, observe everything, and expand scope only when the foundation is solid.

Most agent failures aren't model failures. They're context failures. The agent didn't have enough information about its environment, constraints, or purpose to make the right decisions. Steering files are the most underused tool in agent building — and one of the most powerful.

What is a steering file?

A steering file is a structured document that defines the behavioural parameters of an agent: its purpose, its constraints, how it should handle edge cases, what it should never do, and how it should escalate when uncertain. Think of it as a standing brief — not a one-time system prompt, but a living document that travels with the agent and gives it consistent grounding across every session.

Why most agents skip them

Builders typically start with a system prompt. It gets the agent working in testing. Then the agent ships without anyone formalising the implicit assumptions baked into that prompt. Six months later, when the agent starts drifting or behaving unexpectedly, the team can't diagnose why — because the constraints were never written down. A steering file forces the discipline of making those assumptions explicit.

What a good steering file contains

At minimum, a steering file should cover:

• Purpose statement — what this agent exists to do, in one sentence
• Scope boundaries — what the agent should and should never handle
• Escalation rules — when to stop and ask a human
• Tool usage guidelines — which tools to use, when, and in what order
• Failure modes — how to handle ambiguous input, missing data, or tool errors

Steering files as living documents

The best steering files evolve with the agent. Every time the agent fails in an unexpected way, the failure gets diagnosed and the steering file gets updated. Over time, it becomes a precise specification of the agent's intended behaviour — and a diagnostic tool when something goes wrong.

Multi-agent systems especially need them

In a multi-agent system, steering files become critical. When one agent is handing off to another, both need to understand their respective roles and what constitutes a valid handoff. Without steering files, multi-agent pipelines drift quickly. With them, they stay aligned even as the system grows in complexity.

The agents that perform best in production are rarely the most sophisticated. They're the ones with the clearest steering. Start there.

Claude Code is Anthropic's command-line tool that gives you an AI pair programmer directly in your terminal. For agent builders, it's become one of the fastest ways to go from idea to working prototype. Here's how to actually use it — and the patterns that make it genuinely powerful.

Getting set up

Claude Code runs in your terminal and works best when it has access to your actual codebase. Install it with npm install -g @anthropic-ai/claude-code, then run claude from inside your project directory. It reads your files, understands your context, and can make changes directly — no copy-pasting code into a chat window.

Start with a CLAUDE.md file

Before you ask Claude Code to do anything, create a CLAUDE.md file in your project root. This is your steering file for the coding session. Write down what the project does, what stack you're using, what conventions to follow, and any constraints. Claude Code reads this automatically and uses it to orient every subsequent action. This single step dramatically improves output quality.

Describe outcomes, not steps

The temptation is to give Claude Code step-by-step instructions. Don't. Describe what you want to achieve: "Build an agent that takes a company name, searches for recent news, and returns a three-sentence brief." Let Claude Code figure out the implementation. It frees you to focus on what the agent needs to do rather than how.

Use it for debugging agent loops

Paste the output of a failing agent run directly into Claude Code and ask it to diagnose the problem. Because it has access to your codebase, it can trace the issue through your actual code — not a hypothetical version. Instead of spending an hour debugging, you spend five minutes reviewing the diagnosis.

Iterate fast, commit often

Claude Code works best in short, focused sessions. Ask it to do one thing, review the result, commit if it's right, then move to the next thing. Long sessions with large, ambiguous requests produce worse results than short sessions with precise ones. Treat it like a fast junior developer: give clear tickets, review the work, give immediate feedback.

Teams using Claude Code for agent development consistently report that the bottleneck shifts from implementation to design — which is exactly where your attention should be.