AI Methodologies: Agents Know Nothing

A Practical Method: Make Context a First-Class Input

Agents are most effective when they start with the same inputs a human would use: goals, constraints, relevant code and docs, and the right tools. The output is usually a first draft, so aim to make it easy to verify and hard to misunderstand. A good default is to ask: “With the context, constraints, and tools provided, can this task be completed without guessing?”

Habits:

• Provide the goal and constraints up front (scope, non-goals, SLAs, security boundaries, “do not change” areas).
• Attach the minimum viable context: relevant artifacts, interfaces and contracts, examples, and a definition of “done.”
• Give it the tools and permissions it actually needs (and no more), plus any required runbooks or commands.
• Standardize outputs for review: clear logs, structured results, and links to evidence (change artifacts, verification output, traces).
• Use schemas, interfaces, and consistent structure so the agent has guardrails for changes.
• Require verification artifacts: checks or validation steps that make correctness auditable.