Agentic coding lets an AI plan, edit, and run code across many files on its own. Here is a repeatable workflow that keeps the speed while holding the quality bar.
Agentic coding is the practice of handing an AI assistant a goal instead of a single instruction, then letting it plan the work, edit multiple files, run commands, read the output, and iterate until the goal is met. It is the difference between asking for one function and asking for a working feature. The productivity gain is real, but so is the failure mode: an agent that runs unsupervised for twenty minutes can produce a large, confident, and quietly broken change.
The teams who get value from agentic coding are not the ones with the best prompts. They are the ones with the best process around the agent. This post lays out a workflow that keeps the speed of autonomy while keeping a human in control of the parts that matter.
Start with a spec, not a prompt
The single highest-impact habit is to make the agent write a short specification before it writes any code. A spec forces the ambiguous parts of your request into the open: what the inputs are, what the edge cases are, what "done" means, and what is explicitly out of scope. When you review a spec you are reviewing intent, which is cheap. When you review a diff you are reviewing consequences, which is expensive.
Ask the agent to list its assumptions and open questions at the end of the spec. The assumptions it surfaces are usually where the bug would have been.
Give the agent a tight feedback loop
An agent is only as good as the signals it can act on. If it can run the test suite, the type checker, and the linter, it can catch its own mistakes before you ever see them. If it cannot, every error becomes your problem to find. Wire up the fast, deterministic checks first and make sure the agent knows how to run them. The goal is a loop where the agent proposes a change, runs the checks, reads the failures, and fixes them without your involvement.
- Unit tests and type checks: fast, deterministic, safe to run on every iteration.
- A build step: catches integration errors a single-file view would miss.
- A linter and formatter: keeps the diff readable so your review stays cheap.
- A dedicated review pass: a separate agent that critiques the change rather than defends it.
Separate the author from the reviewer
The most reliable quality gain in agentic coding is to stop asking one model to both write code and judge it. A model reviewing its own output is motivated to defend it. An independent reviewer, given only the diff and the spec, is free to be adversarial. This is the core idea behind TLM Forge: a spec audit before any code, independent multi-agent review of both the plan and the output, and a red-team gate that blocks shipping until critical issues reach zero. You can read how the review council is structured on the how it works section.
| Dimension | Unsupervised agent | Agent with a review gate |
|---|---|---|
| Speed to first draft | Very fast | Fast |
| Confidence in output | Unverified | Independently reviewed |
| Test coverage | Optional, often skipped | Enforced before merge |
| Security review | None | Adversarial red-team pass |
| Human effort | High (you are the QA) | Low (you approve, not hunt) |
Keep memory outside the model
Context windows are large but they are not memory. An agent forgets your architecture decisions the moment the session ends, and re-deriving them wastes tokens and invites drift. Persist the durable facts about your project (conventions, past decisions, gotchas) somewhere the agent can reload them. A dedicated memory layer like MemX keeps that context private and portable across sessions, so the agent starts each task already knowing how your codebase works instead of guessing.
Autonomy is not the goal. A correct, reviewed, tested change is the goal. Autonomy is just how you get there faster.
A workflow you can repeat
- Write the spec first and review the intent, not the code.
- Let the agent implement against fast, deterministic checks it can run itself.
- Route the diff through an independent reviewer that did not write it.
- Block the merge until tests pass and critical findings reach zero.
- Persist what you learned so the next session starts smarter.
Frequently asked questions
01Is agentic coding just a bigger autocomplete?
No. Autocomplete predicts the next token in the file you are in. An agent plans a multi-step change, edits several files, runs commands, reads the results, and iterates. The unit of work is a task, not a line.
02Do I still need to review the code if the agent tested it?
Yes, but the review is different. When the agent runs tests and an independent reviewer critiques the diff, your job shifts from hunting bugs to approving intent. You review the spec and the summary of findings rather than reading every line cold.
03How do I stop the agent from making huge, unreviewable changes?
Constrain scope in the spec, ask for the smallest change that satisfies the goal, and require a review gate that fails on scope creep. A change that touches files outside the spec is a signal, not a convenience.