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How I Use AI Agent Teams to Ship Features on Autopilot

How I Use AI Agent Teams to Ship Features on Autopilot

How I Use AI Agent Teams to Ship Features on Autopilot

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Renas Hassan

Feb 14th, 2026

I've been experimenting a lot with AI-assisted feature development. I've gone through multiple iterations of workflows, trying to find something that consistently produces high quality code without me having to babysit it the entire time. After months of tweaking and experimenting, I think I've found something that actually works. Let me walk you through the system I built.

The problem with single-session AI coding

If you've been using AI to write code, you've probably run into this: you start a task, the AI is sharp and fast in the beginning, but as the conversation grows and the context window fills up, the performance starts degrading. It starts forgetting things you told it 10 minutes ago. It starts making mistakes. It loses track of what it's supposed to be doing.

This is the context window problem, and it was the single biggest bottleneck for me when trying to do anything non-trivial with AI. Long-running tasks like implementing an entire feature would quickly eat up the context. The quality of the output would drop off a cliff, and I'd end up having to intervene constantly to course correct.

On top of that, everything was sequential. One agent doing everything: exploring the codebase, writing the code, writing the tests, reviewing its own work. It's like having one developer do literally everything on a feature by themselves, in one sitting, without taking a break. Of course the quality is going to suffer.

The unlock: agent teams

Then I discovered agent teams in Claude Code. This was the next big unlock for me.

The idea is simple but powerful: instead of one AI session doing everything, you can spawn multiple agents that each have their own session and their own context window. They can do work independently and then report back their findings to a lead agent. This is a game changer because each agent starts fresh with a clean context, focused on one specific task. No more context bloating. No more performance degradation halfway through.

Think of it like this: instead of one overworked developer trying to do everything, you now have a small team where each person owns a specific piece of the work. The lead coordinates, the workers build, and the reviewers check. They communicate with each other through a shared task list and direct messages.

I took this concept and ran with it. I built two custom skills for Claude Code that together form a fully autonomous feature development pipeline. One for gathering requirements, one for implementation.

Phase 1: the interview

Before writing a single line of code, you need to know exactly what you're building. This sounds obvious, but I can't tell you how many times I've seen people (myself included) just jump into coding with a vague idea and then wonder why the AI produced something completely different from what they wanted.

The /interview skill solves this. It's a structured conversation where the AI asks you questions about what you want to build, just like a product discovery session. It goes through four phases:

1. Product interview: what feature, what problem does it solve, who are the users, how do you know it's done?
2. Technical interview: what are the key technical decisions, what data shapes do you need, what framework/language are you using?
3. Spec generation: it produces a single spec document (spec.md) with everything: requirements ordered by priority, acceptance scenarios in Given/When/Then format, technical direction, constraints, and what's out of scope.
4. Guided review: it walks you through the spec in blocks so you can approve or reject each section. No surprises.

The key thing here is that it pushes for specifics. It doesn't let you get away with vague answers. If you say "I want a login page", it's going to ask you what authentication method, what fields, what error states, what happens after login, and so on. This level of detail in the spec is what makes the implementation phase actually work.

Phase 2: the implementation

This is where the magic happens. You run /implement, and it kicks off the agent team. Let me break down what actually happens under the hood.

The lead agent

The lead is the coordinator. It reads the spec, manages the team, creates tasks, assigns work, and handles reviews. Critically, the lead never writes code. It's in delegate mode the entire time. Its only job is to orchestrate. This is important because the moment the lead starts coding, it starts burning context on implementation details instead of focusing on coordination.

Codebase exploration

Before designing any tasks, the lead spawns an explorer agent. This agent scans the codebase and reports back on: existing patterns, architecture conventions, relevant files, integration points, test infrastructure, and build/lint commands. The findings get persisted to an explorer-report.md file so all future agents can reference them.

Why? Because you don't want your AI agents reinventing the wheel. If the codebase already has a specific pattern for API routes, the agents should follow that pattern. If there's already a test helper for mocking the database, the agents should use it.

Reconciliation

Here's something most people skip: the lead compares the spec against the explorer findings and looks for conflicts. Maybe the spec says to use a certain file structure, but the codebase already has a different convention. Instead of silently deviating, the lead stops and asks you to make a decision. This prevents a lot of headaches down the road.

Task design and parallel execution

This is where the parallelism comes in. The lead takes the spec and explorer report, designs testable interfaces, and then breaks the work into self-contained tasks.

In order for multiple agents to work on the same feature in parallel, they need to agree on how their pieces connect. Without this step, the lead has no way to know which tasks depend on each other, which ones can run simultaneously, or how to break the work up in the first place. You have to plan for this before you can delegate anything.

So the lead designs the interfaces first. If Agent A is building the API route and Agent B is building the service layer, they both need to know: what function does B expose? What arguments does it take? What does it return? The lead defines these public contracts up front: function signatures, type definitions, and module boundaries. Each agent then builds to that contract independently, and everything fits together at the end.

Each task gets:

• A clear description of what to implement
• Which files to create or modify
• Existing patterns to follow (from the explorer report)
• Ordered TDD slices (more on this below)
• Success criteria

The key insight I had was that not every task depends on every other task. Within a single feature, some pieces of work are completely independent. The lead identifies which tasks can run in parallel (non-conflicting file sets) and which ones need to wait for dependencies. Tasks that don't conflict with each other get spawned simultaneously, each to its own agent with a fresh context window.

So instead of one agent doing Task 1, then Task 2, then Task 3 sequentially, you get three agents working on all three at the same time. In the end, all their work gets merged together. This is a massive speedup.

Why sliced TDD and not just "write tests"

Here's something I learned the hard way: when you let an AI write a whole batch of tests first and then implement everything at once, the tests are basically useless. The AI already knows what it's about to build, so it writes tests that are designed to pass from the start. Nothing ever actually fails. You end up with a green test suite that gives you a false sense of confidence while the actual behavior might be completely off. And when context starts running low, it gets even worse because the AI will quietly change the tests to fit the code rather than fix the code itself.

The fix is vertical slicing TDD. You force the AI to write one test, watch it fail, then write just enough code to make it pass. One behavior at a time. Because the test has to genuinely fail first, there's no way to cheat. Each cycle builds on what was learned in the last one rather than working off assumptions.

Every teammate follows a strict RED-GREEN-REFACTOR cycle for each TDD slice. This isn't optional, it's baked into the prompt.

For each behavior they need to implement:

1. RED: write one failing test that exercises the public interface. If the test passes immediately, stop and investigate because something is wrong.
2. GREEN: write the minimum code to make that test pass. No speculative code, no building ahead.
3. REFACTOR: clean up, remove duplication, improve names. Run the tests again to make sure nothing broke.

Then move on to the next slice. The slices are ordered from simplest to most complex, so each one builds on the previous. Here's what that looks like in practice:

Notice how each slice adds one specific behavior. The tests only care about what the function does from the outside, not how it works internally. So if you later refactor the guts of the function, the tests still pass as long as the behavior stays the same. No fragile tests that break every time you rename a variable or restructure something.

After completing all slices, each teammate does a self-review before reporting back. They check: did I implement everything the task asked for? Is the code clean? Did I follow codebase patterns? Did I stick to strict RED-GREEN-REFACTOR? Are my tests actually testing behavior?

The two-stage review pipeline

This is what really ensures quality. When a teammate reports completion, the lead doesn't just trust them and move on. It kicks off a two-stage review.

Stage 1: Spec compliance. A reviewer agent reads the actual code (not the teammate's summary) and compares it line by line against the task requirements. Did they implement everything? Did they add stuff that wasn't asked for? Did they misunderstand something? The verdict is either SPEC_PASS or SPEC_FAIL.

Stage 2: Code quality. Only runs if Stage 1 passes. A second reviewer checks: does it follow codebase patterns? Are the tests actually testing behavior? Any bugs, dead code, security concerns? The verdict is QUALITY_PASS, QUALITY_PASS_WITH_ISSUES, or QUALITY_FAIL.

If either review fails, the lead spawns a fixer agent that addresses the specific issues found. Then it re-runs the review. This can happen up to two times. If it still fails after two fix cycles, it escalates to me.

Integration verification

Once all tasks are completed and reviewed, there's one final task: integration verification. This runs the full test suite, linting, and type checking across the entire codebase to make sure nothing conflicts. If something fails, the lead figures out which task caused it and spawns a fix agent targeting that specific area.

What this looks like in practice

Here's what my actual workflow looks like:

1. I run /interview and spend 10-15 minutes answering questions about the feature
2. The spec gets generated and I review it
3. I run /implement
4. I go grab a pepsi, take a walk, work on next feature, whatever
5. I come back and the feature is done. Tests passing, code reviewed, integration verified.

The whole implementation phase runs for anywhere between 30 minutes to over an hour depending on the feature complexity. And I'm not sitting there watching it. It's fully autonomous. The agents explore, code, test, review, fix, and verify all on their own.

The trade-off

I'm not going to pretend this is perfect. Agent teams eat tokens like crazy. Each agent is its own Claude session, so if you have a lead, an explorer, three teammates, and a few reviewers, that adds up fast. Running a full feature implementation can cost a significant amount in API tokens.

But here's how I see it: the alternative is me sitting there for hours, manually guiding a single AI session, constantly course-correcting, dealing with context degradation, and still ending up with code that needs a lot of cleanup. The token cost is worth it when you factor in the time saved and the quality of the output.

Why this works

Looking back at what makes this system effective, it boils down to a few things:

• Fresh context per agent. No context pollution. Each agent starts clean and focused on its specific task.
• Separation of concerns. The lead coordinates, the explorer explores, the teammates implement, the reviewers review. Nobody does everything.
• Parallelism. Non-blocking tasks run simultaneously, cutting implementation time.
• TDD discipline. Strict RED-GREEN-REFACTOR ensures the code actually works and the tests actually test something.
• Independent review. Reviewers don't trust the implementer. They verify independently. This catches issues that self-review misses.
• Spec-driven. Everything traces back to the spec. No scope creep, no guessing.

Try it yourself

I've open sourced the skills so you can try this yourself. Check out SpecOps on GitHub for the full setup instructions, or just clone and copy the skills into your ~/.claude/skills/ directory.

You'll also need to enable agent teams in Claude Code by adding CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS to your settings.

Start small. Try a feature with 2-3 tasks first before going all out with massive parallel implementations. The workflow applies to any codebase: interview first, spec it out, then delegate to a team of agents with clear responsibilities and a review loop.