Stop Letting Your AI Agent Run Naked: 4 Harness Solutions to Tame Claude Code (and Your Sanity)

Core question this article answers: Why do powerful AI coding tools go off the rails the longer they run—and what can you do about it?

Have you ever had this happen? You ask Claude Code to refactor a module. It writes 5 files, then tests fail. It starts debugging frantically, stuffing temporary patches, error logs, and abandoned solutions into the codebase. By the time it reaches the 10th file, it can’t tell the difference between the current task and the historical noise—it starts treating a discarded approach as a new instruction. It circles endlessly.

This isn’t a model weakness. This is the cost of running naked.

“Running naked” means throwing Claude Code into your codebase with zero engineering constraints. You’ll hit three walls fast:

  1. Context decay – The longer the task, the more debug logs accumulate. The agent mistakes old solutions for new instructions. You don’t notice until your git diff explodes.
  2. No enforced process – Plan Mode is a “suggestion”, not a “gate”. The agent can skip spec writing, testing, and review anytime. By the time you notice, the codebase is a mess.
  3. Single perspective – coding only – You ask it to write code, so it charges ahead from a coder’s viewpoint. No one stops it from product, security, or QA angles. The result: code that “works” but is completely unusable.

This is not a model problem. It’s an engineering-constraint problem.

The four Agent Harness solutions I’m sharing today—Super Powers, GSD, G-Stack, Archon—are the reins you put on the wild horse of AI. They don’t make Claude smarter. They add back the missing gates in your development process.

A cross-domain reference point: Think of this like a Sketch comedy show (e.g., Saturday Night Live). A good sketch doesn’t rely on one joke for 5 minutes. It uses a “three-beat” structure—each beat escalates conflict and rhythm. A naked AI agent is like a one-joke sketch: funny for 30 seconds, then repetitive and painful. Harness is the writer’s metronome that forces every segment to have a beginning, middle, and payoff.

Let’s break it down, layer by layer.

What Is a Harness? – Putting Reins on the Wild Horse of AI

Core question this section answers: What exactly is a Harness, and how is it different from prompt engineering?

The word “harness” comes from horse tack—the equipment used to control a horse.

A large language model is like a powerful horse. Without intervention, it runs wild, hallucinates, and can’t reliably produce what you want. The system that controls it is the Harness.

Formula:

Harness = Agent - Model

Everything in an agent except the LLM itself—that’s the Harness.

There’s a three-layer progression here:

  • Prompt Engineering – How to ask questions.
  • Context Engineering – How to structure information.
  • Harness Engineering – How to build the system around the model. You study everything except the model itself.

A Harness solves the three problems above: context decay, missing process, and single perspective.

These four solutions are not four competitors on the same track. They are four layers of answers in one agent system: Discipline layer, Context layer, Role layer, Orchestration layer.

Solution Core Layer Core Mechanism One-line positioning
Super Powers Discipline 7 mandatory stages Turns “suggestions” into “gates”
GSD Context Atomic tasks + fresh 200k token context Gives long tasks a clean room
G-Stack Role 23 expert roles, multi-perspective review Makes AI “argue with itself”
Archon Orchestration YAML DAG workflows + observability Turns process into code

Layer 1 | Super Powers: Turning “Suggestions” into “Gates” (Discipline Layer)

Core question this section answers: How do you make an AI follow a process without skipping steps, using the lightest possible approach?

Super Powers solves with discipline.

It breaks engineering habits into 7 mandatory stages:

  1. Brainstorming – Think about what to do. Don’t touch code yet.
  2. Git Worktrees – Isolate experiments with worktrees. Don’t pollute the main branch.
  3. Planning – Write a spec and PLAN. Let a human review it.
  4. Execution – Start coding.
  5. TDD – Write tests first, then make them pass (RED-GREEN-REFACTOR).
  6. Code Review – Review yourself, then have AI review.
  7. Branch Completion – Confirm tests pass, merge the branch.

These 7 stages are hard constraints, not suggestions. Each stage has a corresponding Markdown Skill. The agent must complete them in order.

Key difference: Turns “suggestions” into “gates.” Plan Mode is a soft constraint. Super Powers is a hard workflow.

Real-world example (from the original)

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I used it to write a data export feature. After the agent finished writing code in the Execution stage, it automatically entered TDD, realized I hadn’t provided test data, and stopped to ask for mock data. With a naked Claude Code, that’s impossible—it would have invented fake data and kept going.

When to use

  • Daily tasks that take under 30 minutes
  • Individual development, no heavy systems needed
  • Quick setup, low cost, certain return

Biggest pitfall

It’s still Markdown Skills—essentially prompt constraints. If the model’s context gets too long or instructions conflict, it might still skip a stage.

Personal reflection

When I first used Super Powers, I thought “7 stages is too verbose.” But after two runs, I saw the value: that verbosity saved me. Once, after the Execution stage, the agent auto-entered TDD and found a test error. It stopped and asked for confirmation—instead of committing a broken version like it used to.

My verdict: Super Powers is the best first-layer baseline. Not because it’s the most powerful, but because it’s the lightest, easiest to roll back, and perfect as the first discipline layer for personal AI coding.

Layer 2 | GSD: Giving Long Tasks a “Clean Room” (Context Layer)

Core question this section answers: Why does AI start going crazy when a task runs long (2–3 hours), and how do you fix it?

GSD doesn’t solve small changes. It solves long tasks that take 2–3 hours and touch a dozen files.

Its core insight: Context decays.

You ask the agent to refactor a module. It changes 5 files, tests fail, it starts debugging. Debugging produces temporary code, error logs, abandoned solutions. These pollute the context. By the 10th file, the agent can’t tell current task from historical noise.

GSD’s solution is direct: Give every atomic task a fresh 200k token context.

It breaks long tasks into a 6-command loop:

  1. /gsd-new-project – Create project, initialize artifacts (persistent storage)
  2. /gsd-discuss-phase – Discuss requirements, clarify goals
  3. /gsd-plan-phase – Break down tasks, write execution plan
  4. /gsd-execute-phase – Execute one atomic task (fresh context)
  5. /gsd-verify-work – Verify results, run tests
  6. /gsd-ship – Confirm completion, archive artifacts

Every time you run /gsd-execute-phase, it spins up a new subagent with a clean 200k token context. After execution, it writes results back to artifacts, then moves to the next task. Task #50 is never polluted by the debug noise from earlier tasks.

Real-world example (from the original)

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I used it to refactor a 15-file module, split into 8 phases. Each phase had a fresh context. The agent wasn’t distracted by previous debug logs. All 8 phases passed tests. The git diff was as clean as hand-written code.

When to use

  • Complex projects with long-running tasks (2–3 hours)
  • You have test assets and can afford token costs
  • Need cross-session recovery (persistent artifacts)

Cost

Splitting tasks, launching subagents, and writing artifacts all increase token costs. It’s insurance for long tasks, not something you need for every small request.

Personal reflection

The first time I used GSD, I thought “splitting tasks is a cost itself.” But after a 3-hour refactor, I did the math: without GSD, Claude would start circling by hour 2, wasting at least an hour of token spend and a lot of my time. The extra token cost of GSD was far less than the time I would have spent manually correcting the mess.

One-line summary: GSD doesn’t make you faster—it keeps you from going off course.

Layer 3 | G-Stack: Making AI “Argue with Itself” (Role Layer)

Core question this section answers: How do you stop the AI from heading in the wrong product direction? How do you make it get “critiqued” before writing code?

G-Stack doesn’t solve technical problems. It solves decision problems.

Most AI coding failures aren’t about failing to write code. They’re about very reliably heading in the wrong direction. You ask for a user permission system, and it writes a single-tenant solution—completely ignoring multi-tenant isolation. You ask for a payment API, and it forgets security auditing entirely.

G-Stack’s core is 23 expert roles, covering product, engineering, security, QA, operations, and more. Each role has its own prompt and review criteria.

The workflow has 7 stages:

  1. Think – Use /office-hours to bring together CEO, PM, and engineer roles to discuss requirements.
  2. Plan – Write a solution, use /plan-ceo-review for CEO review.
  3. Build – Write code.
  4. Review – Use /qa to let QA pick holes.
  5. Test – Run tests.
  6. Ship – Deploy.
  7. Reflect – Use /cso for a security officer risk review.

Each stage can summon different roles. For example, /office-hours launches CEO, PM, and engineer roles simultaneously, critiquing from product value, technical feasibility, and resource investment angles.

Real-world example (from the original)

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I used it to design a user permission system. After writing the plan, I ran /plan-ceo-review. The CEO role asked: “Is this permission system for internal use or customer use? If it’s for customers, why didn’t you consider multi-tenant isolation?” I realized I hadn’t thought through the boundaries at all.

When to use

  • Solo developers and founders lacking team review
  • Product direction tends to waver
  • Need multi-perspective decisions (product, security, operations)

Caution

Don’t blindly install all 23 roles. Only pull the roles for the perspectives you lack. Missing security? Use /cso. Missing product thinking? Use /office-hours. Going all-in increases decision noise.

Personal reflection

I initially thought G-Stack was “too dramatic”—why hold a “CEO review” just to write code? But after one permission system run, I was convinced. Without that CEO role’s question, my system would have gone live and customers would have complained about missing multi-tenancy. I wouldn’t have thought of it myself—because I was coming from a tech perspective, naturally blind to product boundaries.

One-line summary: G-Stack doesn’t make AI smarter. It stops AI from making low-level product mistakes.

Layer 4 | Archon: Turning Process into Code (Orchestration Layer)

Core question this section answers: How do you let teams run AI workflows at scale and repeatedly? How do you turn one person’s experience into team templates?

Archon is the heaviest of the four, and the most future-like.

It writes workflows as YAML DAGs (Directed Acyclic Graphs). Each step is a node, with dependencies between nodes.

A typical Archon workflow looks like this:

workflow:
  - name: analyze_requirements
    agent: analyst
    output: requirements.md
  
  - name: write_code
    agent: developer
    depends_on: [analyze_requirements]
    output: src/
  
  - name: write_tests
    agent: tester
    depends_on: [write_code]
    output: tests/
  
  - name: review
    agent: reviewer
    depends_on: [write_code, write_tests]

Each node can run in parallel (if no dependencies), isolated with git worktrees. For example, write_code and write_tests can run simultaneously. After execution, they merge back to the main branch.

Archon comes with 17 default workflows covering common scenarios: new feature development, bug fixing, refactoring, documentation generation, and more. You can also write your own YAML to define new workflows.

It also provides a Dashboard for observability, with multi-platform access (CLI, Web, Slack, Telegram, Discord).

Real-world example (from the original)

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I used it to process 10 similar bugs. Each bug followed “read log → locate problem → write test → fix → verify”. I wrote a YAML workflow and ran it 10 times, changing only the input parameters each time. Saved a ton of repetitive communication.

When to use

  • Team collaboration, batch tasks
  • 10 issues with the same repetitive process
  • Need to scale (run the same workflow 100 times)

Cost

Installation, maintenance, template quality, and node quality become real costs. It’s not suitable for a beginner to go all-in on day one. You need to understand DAGs, git worktrees, and YAML configuration first.

Personal reflection

The biggest impact Archon had on me: it turns “personal experience” into “team templates.” I used to have to explain the AI workflow to new developers verbally. Now I write one YAML, and newcomers run it directly. But the trade-off is real—debugging a single YAML workflow can take as long as writing the code three times manually. So Archon is for scaled, repetitive scenarios, not one-off exploration.

One-line summary: Archon is for teams, not for individuals just trying things out.

How to Choose? Start Light, Then Stack, Layer by Layer

Core question this section answers: Faced with these 4 solutions, which one do I start with? Or do I need all of them?

Whether a Harness is worth installing depends on which engineering pain is currently hurting you the most.

The most practical path is light first, heavy later:

Your pain point First choice Why not heavier
Daily dev, AI often skips process Super Powers Lightest, 5-min setup, cheap to roll back
Long tasks (2–3 hrs) start circling GSD Specialized for context decay, others don’t fix root cause
Product direction wavers, produces unusable solutions G-Stack (pull only needed roles) Take only perspectives you lack, don’t install all 23
Team batch tasks, 10 repetitive Issues Archon Heavy, but saves time at scale

Over time, the four aren’t mutually exclusive—they stack into four layers:

  • Bottom layer: Discipline (Super Powers) – stops the agent from running wild.
  • Second layer: Context (GSD) – keeps long tasks from decaying.
  • Third layer: Role (G-Stack) – prevents single perspective.
  • Top layer: Orchestration (Archon) – lets teams standardize processes.

But order matters – don’t chase the full stack on day one. Start with Super Powers for a week. If long tasks become a problem, add GSD. If product decisions start wavering, pull a specific G-Stack role. Only when your team scales, consider Archon.

My Final Reflection: The Core of AI Coding Is Not the Model—It’s Engineering

Core question this section answers: Isn’t all this Harness stuff over-engineering? Can’t I just rely on a smarter model?

I’ve spent a lot of time testing these four solutions. One uncomfortable truth: Model IQ is growing fast, but my engineering constraints are always playing catch-up.

Every time a new Claude release drops, I fantasize: “Maybe this time I won’t need a Harness.” What happens? It writes code faster, but it also circles faster. The context window gets larger, but there’s more decaying information inside it.

This isn’t a model problem. It’s an engineering law that applies to any intelligent system: freedom without constraints is chaos.

Super Powers’ 7 stages, GSD’s clean contexts, G-Stack’s multi-role arguments, Archon’s YAML workflows—they’re not limiting AI. They’re translating proven human engineering gates into a language that AI can execute.

Which do you lack most right now: discipline, context management, multi-perspective review, or process orchestration?

My advice: start with the one that hurts the most. But don’t stop at just one. Because your tasks will get longer. Your product will get more complex. Your team will grow. The four-layer stack is the destination.

Practical Summary / Action Checklist

If you only have 5 minutes, remember these 5 points:

  1. Harness = Agent – Model – It’s all the engineering constraints that control an LLM.
  2. Super Powers for daily dev – 7 mandatory stages turn “suggestions” into “gates.”
  3. GSD for long tasks – Each atomic task gets a fresh 200k context, solving context decay.
  4. G-Stack for wavering product direction – Multi-role review (CEO/PM/QA/security) blocks wrong decisions.
  5. Archon for team batch tasks – YAML DAGs define reusable workflows.

Roadmap:

  • Week 1: Install Super Powers. Run 3 daily tasks. Observe if it skips steps.
  • Week 2: If tasks often exceed 1 hour, add GSD. Break long tasks into phases.
  • Week 3: If solutions get rejected by product/security, pull G-Stack’s /office-hours or /cso roles.
  • Week 4: If your team has 10+ repetitive Issues, try Archon with your first YAML workflow.

One-page Summary

Problem Solution Core Mechanism One-line
AI skips process, jumps into code Super Powers 7 mandatory stages Turns “suggestions” into “gates”
Long tasks decay context, circle GSD Atomic tasks + fresh 200k context Gives each task a clean room
Product direction wavers, wrong decisions G-Stack 23 expert roles, multi-perspective review Makes AI “argue with itself”
Team repetition, scaling difficulty Archon YAML DAG workflows + observability Turns process into code

Selection formula:

  • Individual + short tasks → Super Powers
  • Individual + long tasks → Super Powers + GSD
  • Individual + wavering direction → Above + G-Stack (pull needed roles)
  • Team + batch → Above + Archon

FAQ

Q1: Can I use just one of these and skip the others?

Yes—as long as your current problem is exactly what that solution covers. If your tasks get longer, you’ll likely add GSD later. The four layers are not mandatory to install together; they’re addable on demand.

Q2: Won’t Super Powers’ 7 stages slow me down?

No. It forces you to think, which saves the time you would have spent fixing bugs later. If the task is truly tiny (e.g., changing one config line), you can quickly confirm during the Brainstorming stage and skip details.

Q3: How much extra token cost does GSD add?

It depends on task length and how finely you split phases. In the original 15-file, 8-phase refactor example, token cost was about 1.5x a naked run. But it saved at least an hour of manual correction—high ROI.

Q4: Do I need to install all 23 G-Stack roles?

Absolutely not. Only install the perspectives you lack. Missing security? Pull /cso. Missing product thinking? Pull /office-hours. Installing too many adds decision noise.

Q5: How much DAG and YAML knowledge do I need for Archon?

Basic level is enough. Understand how depends_on works and how to define node outputs. The example in this article is the most common pattern. Don’t start with complex DAGs.

Q6: Won’t new models make these Harnesses obsolete?

No. Stronger models need stronger engineering constraints. Like a faster race car needs better brakes and safety gear. Harnesses aren’t patches for weak models—they’re infrastructure for any AI engineering system.

Q7: Can I mix features from different solutions?

Yes. The four-layer stacking described in this article is exactly that combination. For example, you can use Super Powers’ 7 stages, use GSD’s phase splitting for long tasks, and pull G-Stack’s /cso for security review.

Q8: Which solution is best for a beginner to start on day one?

Super Powers. It’s the lightest, takes 5 minutes to configure, and has the lowest risk. Run it for a week, then decide if you need to add others.