The Complete Guide to Harness Engineering: Building AI Agents the Claude Code Way

The “just throw a prompt at ChatGPT” era is over. Since 2025, the center of gravity in AI engineering has shifted rapidly toward harness engineering. It is one of the most frequently repeated keywords in Anthropic’s internal blog posts and in OpenAI’s agent research.

Yet ask someone “what is a harness?” and few can answer crisply. In this article we unpack harness engineering using runnable code and Claude Code’s own architecture as the case study. By the end, you will have everything you need to build your own agent from scratch.

A harness is the “scaffolding” that surrounds an AI

“Harness” originally referred to tack for a horse or a safety belt for a climber. In software, think of a “test harness”: the outer scaffolding that makes something actually run.

In the AI world, a harness is the wrapper layer surrounding an LLM. Concretely, it bundles everything the model needs in order to operate on real-world tasks:

• Tools: read files, execute commands, call APIs, and so on
• Context management: what to remember, what to forget, what to compress
• Control loop: when to call, when to stop, when to retry
• Permissions and safeguards: prevent destructive operations from running unattended
• Memory: knowledge that persists across sessions

A prompt is just one input into this harness. With a weak harness, even the cleverest prompt hits a performance ceiling. This is why we increasingly hear “prompt engineering alone is not enough.”

Why the harness matters: think in OODA loops

An LLM on its own can only “generate the next token.” To solve real-world tasks, you must spin an OODA loop (Observe → Orient → Decide → Act) borrowed from military strategy.

Phase	Description	Owner
Observe	Read the environment (files, DB queries)	Harness
Orient	Shape the information and hand it to the LLM	Harness
Decide	Pick the next move	LLM
Act	Execute (run commands, call APIs)	Harness

As you can see, three of the four phases belong to the harness. The LLM is only strong at Decide. The quality of the scaffolding around it determines the quality of the whole agent.

Three harness levels, by example

Let’s solve the same “generate a blog post” task at three escalating harness levels.

Level 1: Raw API call (almost no harness)

import Anthropic from "@anthropic-ai/sdk";
const client = new Anthropic();

const res = await client.messages.create({
  model: "claude-opus-4-6",
  max_tokens: 4096,
  messages: [{ role: "user", content: "Write a blog post" }],
});
console.log(res.content[0].text);

Outcome: generic, hollow prose. Every run produces a different topic and structure.

Level 2: Add tools (medium harness)

const tools = [
  {
    name: "read_existing_posts",
    description: "Return the list of existing blog posts with their titles",
    input_schema: { type: "object", properties: {} },
  },
  {
    name: "write_post",
    description: "Write out an MDX file",
    input_schema: {
      type: "object",
      properties: {
        slug: { type: "string" },
        frontmatter: { type: "object" },
        body: { type: "string" },
      },
      required: ["slug", "frontmatter", "body"],
    },
  },
];

async function runAgent(userGoal: string) {
  let messages = [{ role: "user", content: userGoal }];
  while (true) {
    const res = await client.messages.create({
      model: "claude-opus-4-6",
      max_tokens: 4096,
      tools,
      messages,
    });
    if (res.stop_reason === "end_turn") break;

    // The harness executes the tool call
    const toolUse = res.content.find((c) => c.type === "tool_use");
    const result = await executeTool(toolUse.name, toolUse.input);
    messages.push({ role: "assistant", content: res.content });
    messages.push({
      role: "user",
      content: [{ type: "tool_result", tool_use_id: toolUse.id, content: result }],
    });
  }
}

Outcome: a non-duplicate topic is picked and a correctly structured MDX file is produced. Just adding tools dramatically changes quality.

Level 3: A full Claude Code-grade harness

• Autonomous loop (user approval, error retries)
• Context compression (summarize long conversations to save tokens)
• Subagent delegation (translate in an isolated context)
• Prompt caching (do not resend the static prefix)
• Hooks (auto-lint before commit)

Wiring all of that by hand is a serious project. That is exactly why studying Claude Code as a reference implementation pays off.

Dissecting Claude Code’s harness

Claude Code is the most polished agent harness inside Anthropic. It decomposes into the following five layers.

Layer 1: Tool design

Tools like Read, Edit, Write, Bash, Glob, Grep, and Agent ship out of the box. Pay attention to their granularity:

• Grep is not plain grep but a ripgrep wrapper — accurate and fast
• Edit is not a whole-file rewrite but a targeted string replacement — minimal diff
• Agent spawns a subagent and isolates its context

Tool quality maps directly to agent quality. “Anything that works” is not good enough. Design tools with idempotency, clear error messages, and a single responsibility in mind.

Layer 2: Layered context

~/.claude/CLAUDE.md         ← global rules
./CLAUDE.md                 ← project rules (auto-loaded)
~/.claude/memory/           ← long-term memory (across sessions)
  ├── user_profile.md
  ├── feedback_xxx.md
  └── project_xxx.md
conversation history         ← recent turns
tasks/plan                   ← progress in the current session

Each layer has a different lifetime and purpose. Writing to the wrong place loses information quickly, or worse, keeps stale data alive. Use tasks for “this session only” and memory for “reusable across sessions.”

Layer 3: Subagent delegation

With the Agent tool you can spawn another agent in its own context.

# Main gives directives; the heavy lifting goes to the subagent
Agent(
  subagent_type: "general-purpose",
  prompt: "Translate blog/harness.mdx into English plus 8 more languages,
           save each under blog-{lang}/, then report back"
)

This keeps the main context from being polluted by noisy logs. Long build logs, intermediate translations, search dumps — any work where “you only want the deliverable” — can be offloaded wholesale.

Layer 4: Hooks (deterministic processing)

.claude/settings.json lets you hook shell commands before and after tool calls.

{
  "hooks": {
    "PostToolUse": [
      {
        "matcher": "Edit|Write",
        "hooks": [
          { "type": "command", "command": "npx tsc --noEmit" }
        ]
      }
    ]
  }
}

Type-checking now runs automatically after every edit. Anything that “should be handled deterministically rather than asked of the LLM every time” belongs in a hook.

Layer 5: Permission modes

{
  "permissions": {
    "allow": ["Read", "Grep", "Glob"],
    "deny": ["Bash(rm -rf*)", "Bash(git push --force*)"],
    "ask": ["Write", "Edit", "Bash"]
  }
}

Explicitly deny destructive commands, and require approval for writes. Accidents happen when something runs unattended, so this layer determines your operational safety.

Five pitfalls to watch for

1. Too many tools Give the model 30 tools and it will flounder over which to pick, degrading accuracy. The rule of thumb is 5-15. Push overflow capability into subagents.

2. Not exploiting prompt caching Skip the Claude API’s cache_control and you resend your long system prompt in full every turn — and pay for it. Mind the 5-minute TTL and cache the static parts.

messages: [{
  role: "system",
  content: [
    { type: "text", text: longStaticInstructions,
      cache_control: { type: "ephemeral" } },   // ← this line
    { type: "text", text: dynamicContext },
  ],
}]

3. Error messages the LLM cannot read A tool that returns only Error: undefined cannot be self-repaired by the model. Say what is wrong and how to fix it.

throw new Error(
  `File '${path}' does not exist. ` +
  `Files currently in scripts/: ${list.join(", ")}`
);

4. Skipping human approval Auto-approving destructive actions (delete, force push, DB updates) guarantees that disaster arrives eventually. Default to “ask for writes, deny for deletes.”

5. Never tidying memory Stale information keeps pulling the agent toward wrong assumptions. Memory needs regular pruning too (in Claude Code, use /compact or edit files manually).

Run your own mini harness

Finally, here is a minimal harness you can run locally with Node.js + TypeScript.

// mini-harness.ts
import Anthropic from "@anthropic-ai/sdk";
import { readFileSync, writeFileSync } from "fs";

const client = new Anthropic();

const tools = [
  { name: "read_file",
    description: "Read a text file",
    input_schema: { type: "object", properties: { path: { type: "string" } }, required: ["path"] } },
  { name: "write_file",
    description: "Write out a text file",
    input_schema: { type: "object", properties: { path: { type: "string" }, content: { type: "string" } }, required: ["path", "content"] } },
];

const executors = {
  read_file: ({ path }) => readFileSync(path, "utf-8"),
  write_file: ({ path, content }) => { writeFileSync(path, content); return `written ${path}`; },
};

async function loop(goal: string, maxSteps = 10) {
  const messages: any[] = [{ role: "user", content: goal }];
  for (let i = 0; i < maxSteps; i++) {
    const res = await client.messages.create({
      model: "claude-opus-4-6", max_tokens: 4096, tools, messages,
    });
    messages.push({ role: "assistant", content: res.content });
    if (res.stop_reason === "end_turn") return res.content;

    const toolUse = res.content.find((c: any) => c.type === "tool_use") as any;
    if (!toolUse) return res.content;
    const result = executors[toolUse.name](toolUse.input);
    messages.push({
      role: "user",
      content: [{ type: "tool_result", tool_use_id: toolUse.id, content: String(result) }],
    });
  }
}

await loop("Read README.md and save a 3-line summary as TL;DR.md");

That alone gives you a mini-agent that can read an existing file and write out a new one. Add a Grep tool, a Bash tool, and an Agent tool on top, and you have a miniature Claude Code.

Summary: from prompt author to harness architect

Old mindset	New mindset
A great prompt yields a great output	A great harness yields a great output
Pick a model	Design model + tools + context + permissions
One-shot questions	Continuous loop operation

Claude Code is the best teaching material for absorbing this shift in perspective. Don’t just use it — break it apart and fold the ideas into your own agent. That is the posture required of AI engineers from 2026 onward.

Start by copy-pasting the mini harness above and running it. Ten minutes from now, you will have taken the first step toward your very own agent.

Related articles

• 10 Subagent Patterns in Claude Code
• CLAUDE.md Best Practices
• Token Optimization Techniques for Claude Code
• Claude Code Permissions Guide

References

• Anthropic Agent SDK documentation
• Claude Code official guide
• Prompt Caching guide