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Context Beats Models: The 10-Step Playbook for AI Agents That Actually Work

March 4, 2026

Here are the steps that have consistently worked well with AI agents across tools and models:

  1. Define the task clearly
    Make sure you understand the task and its context.
  2. Load the right context
    Ask the agent to analyze relevant parts of the codebase, documentation, error tracker, or other sources. Add a brief description of what you want to solve so the agent can also explore adjacent areas it thinks are relevant. Using a /plan command can be powerful - it makes your intention explicit and produces a clear, high-level overview.
  3. Write a specification and feed it to the agent
    Clearly define the challenge and the expected outcome. Also mention important architectural requirements.
  4. Ask the agent to self-review the plan
  5. Execute the plan
    If you're not happy with the plan, redo any of the previous steps. The first few times, follow the agent closely to ensure it doesn't do anything silly.
  6. Teach the agent
    During execution, notice where the agent does silly things. Every time it happens, interrupt it. Guide it, and ask it to write the lesson down in agents.md (or a skill file). This makes it more likely the mistake won't happen again.
  7. Agent review
    Ask the agent to review the outcome.
  8. Human review
    Now you can review a task that's already 80%+ complete.
  9. Iterate with the agent
    Ask the agent to fix issues - or discuss the outcome until it's solid.
  10. Send for review - and profit

These steps work because we iterate on the same thing multiple times and look at it from different angles.

Context Is Everything

But one thing matters more than anything else: context.

The context you provide the agent is more important than the model - and even more important than the steps above. To provide great context, you need a solid architectural understanding of the application and a clear understanding of the problem you're asking the agent to solve.

If you don't understand the problem and the context, you can't provide the right inputs - and the agent will fail. In that situation, take a step back and spend time understanding the system and the problem first. Only then will you be able to use an AI agent effectively.