Learn by doing. Build real systems. Keep what works.
Walk from a business goal through manual work, tools, and deterministic workers before introducing an AI agent. Build a TypeScript payment reconciliation harness that boycotts the agent whenever plain code is enough.
An agent without a clear goal or completion check will take the easiest path and claim the work is done while leaving a mess behind. Build typed goal specifications, approval gates, and independent outcome verifiers.
A smart generalist will still guess badly in a specialist domain. Route tasks to the right model, inject reusable SKILL.md playbooks and search tools, enforce domain boundaries, and verify outputs with specialist checks.
An agent needs private facts and current state to do today's job, which makes it a target for prompt injection and data leaks. Build scoped context assemblers, withhold secrets, and verify inputs and outputs.
An agent that can act on your systems can also touch rooms, money, or records it has no business touching. Expose narrow typed tools, enforce least-privilege permissions, and validate every tool call in code before execution.
Because an agent does not pay the bill, it can sit in a loop burning tokens and money for hours. Enforce hard caps on turns, tokens, Naira spend, and wall-clock time, and verify usage on every turn.
When a single agent is no longer enough, you assemble a team of specialists. Coordinate multi-agent workflows through structured file handoffs, active work boards, and human release gates.
