Agentic Methods for a Tech Lead

Agentic Methods: Coding With AI Agents, Designing For Agents TL;DR "Agentic methods" covers two distinct things colliding right now: AI agents that code alongside the team (read, write, run, verify, in a loop), and agentic architectures we design into our own systems (orchestrating autonomous agents on the product side). In both cases, the same principle applies: an agent is only useful if the contract around it is explicit — scope, errors, permissions, stopping points. The Tech Lead role doesn't disappear, it shifts: fewer lines typed, more specification, review, and governance. The underlying topic isn't tooling, it's clarity — exactly like a well-modelled business workflow. Table of Contents Introduction — one word, two meanings Coding with AI agents: what actually changes From autocomplete to the agentic loop The developer's role shifts toward review Explicit guardrails Designing agentic architectures An agent is a box with a contract Orchestration or autonomy: a choice, not a default Observability: if you can't replay it, you can't debug it Where humans remain irreplaceable A Tech Lead checklist for adopting these methods Conclusion — agents reveal a team's maturity Introduction — one word, two meanings "Agentic" has been everywhere for a few months, but it means two different things depending on who's talking: Coding with AI agents : a tool that reads code, writes diffs, runs commands, launches tests, and iterates until it reaches a correct result — instead of suggesting one line at a time. Designing agentic systems : a software architecture where autonomous agents (often themselves LLM-based) make decisions, call tools, and cooperate to accomplish a business task — a support chatbot that triggers refunds, a document pipeline that routes complex cases to a human on its own. These are two separate topics, but the same underlying principle runs through both: an agent — human, AI, or service — is only reliable when it operates inside an explicit frame. It's the s