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Agent Harnesses: Why 2026 Isn't About More Agents — It's About Controlling Them

AI Software Architecture Multi-Agent Systems DevOps

The Agent Sprawl Problem Nobody Talks About

Here’s a number that should concern every engineering leader: the average enterprise now deploys 12 AI agents, and that number is projected to hit 20 by 2027. But according to Salesforce’s 2026 Connectivity Benchmark, only 27% of those agents are connected to the rest of the stack. The other 73%? Shadow agents — unmonitored, ungoverned, and accumulating technical debt faster than anyone wants to admit.

Microsoft’s own telemetry tells a similar story: over 80% of Fortune 500 companies have active AI agents, many built with low-code tools by teams that never coordinated with platform engineering. Gartner is already calling for AI Agent Management Platforms to contain the sprawl.

We solved “how to build agents” in 2025. The real engineering challenge of 2026 isn’t building more agents — it’s building the infrastructure that controls them. That infrastructure has a name: the agent harness.

What Is an Agent Harness?

An agent harness is the control plane that wraps around an AI agent’s execution. It doesn’t replace your agent framework — it governs it. Think of it as the difference between writing a container and running Kubernetes. The container does the work; the orchestrator decides if, when, and how that work is allowed to happen.

Anthropic’s engineering team describes it well: the harness manages the agent’s lifecycle, context window, tool access, and safety boundaries. It’s the software that sits between the LLM and the outside world, making decisions the model itself can’t make — like “should this tool call be allowed?” or “has this agent burned through its cost budget?”

This is distinct from agent frameworks like LangChain or CrewAI, which help you build agents. As Analytics Vidhya’s taxonomy puts it: frameworks provide building blocks, runtimes execute workflows, but harnesses enforce control. If you’ve ever built a god prompt that tried to do everything, you already know why that separation matters.

Agent Sprawl Is the New Technical Debt

We’ve seen this movie before. A decade ago, every team spun up microservices without coordination, and we ended up with service meshes, API gateways, and platform teams to contain the sprawl. Agent sprawl follows the same pattern — except the blast radius is worse because agents make autonomous decisions.

The parallel is precise: microservices sprawl → service mesh, agent sprawl → agent harness. Without centralized governance, you get inconsistent safety policies, duplicated LLM spend, unmonitored tool access, and compliance gaps that no audit can catch after the fact. If you’re already thinking about how agentic systems change your DevOps practice, the harness is where that thinking becomes code.

CNCF’s Four Pillars of Platform Control

The CNCF’s 2026 forecast lays out a framework I keep coming back to: the Four Pillars of Platform Control. Originally written for autonomous infrastructure, they map perfectly to agent management:

  1. Golden Paths — curated, pre-approved configurations. For agents, this means blessed model/provider combos, approved tool sets, and standardized harness configs that teams inherit instead of inventing from scratch.
  2. Guardrails — hard policy enforcement that can’t be overridden. Cost ceilings, duration limits, blocked output patterns, tool allowlists. The agent doesn’t get to negotiate.
  3. Safety Nets — automated recovery when things go wrong. Retry with exponential backoff, fallback responses, circuit breakers. The system degrades gracefully instead of failing loudly.
  4. Manual Review — human-in-the-loop gates for high-stakes decisions. When the agent’s confidence is low or the output touches sensitive systems, a human approves before it ships.

The key insight is that these pillars work together. Golden paths reduce the surface area that guardrails need to cover. Safety nets catch what guardrails miss. Manual review handles the edge cases that automation can’t.

Building It: @htekdev/agent-harness

I didn’t want to just write about agent harnesses — I wanted to build one. So I created @htekdev/agent-harness, a TypeScript library that implements all four CNCF pillars. Here’s what I learned.

The Harness Must Own the Loop

My first version wrapped a single function call with pre/post guardrails. It worked, but it wasn’t a harness — it was middleware. A real agent harness owns the agentic loop: the agent thinks, proposes an action, and the harness decides whether that action executes. Every iteration gets checked against budgets, policies, and safety rules.

const result = await harness.runAgent({
  systemPrompt: 'You are a security engineer reviewing code.',
  input: 'Review the /src directory for vulnerabilities.',
  tools: [listFiles, readFile, searchCode, applyFix],
});

// The harness controlled 4 iterations:
// 1. list_files → discovered project structure
// 2. read_file ×3 → read auth, users, db source
// 3. search_code → found hardcoded secrets
// 4. final answer → security review with findings
// result.iterations tracks every step for observability

The allowedTools guardrail blocks any tool not in the list — before execution. In my tests, when the LLM tried calling execute_sql (not in the allowlist), the harness intercepted it, sent a [BLOCKED] message back to the LLM, and the agent adapted its approach without human intervention. That’s control at every iteration, not just at the boundary.

What Happens Inside the Loop

Here’s the part that took me the longest to get right. My first version of the harness wrapped a single function call — harness.run(myAgentFn). Pre-guardrails, execute, post-guardrails. It worked, but it was middleware, not a harness. The agent could do whatever it wanted inside that function call. The harness was blind to it.

The real design runs the loop itself. Each iteration follows the same cycle:

  1. Check budgets — has the agent exceeded its time, cost, or iteration limit?
  2. Compact context — is the conversation approaching the token ceiling? If so, compress it automatically.
  3. Call the LLM — send the accumulated conversation with tool definitions.
  4. Inspect the response — check the output against blocked patterns. If the LLM says DROP TABLE, the iteration is killed before anything executes.
  5. Validate tool calls — if the LLM requests a tool, check it against the allowlist. Blocked tools get a [BLOCKED by harness] message injected back into the conversation so the agent can adapt.
  6. Execute approved tools — run the tool, then check the output against blocked patterns too. A tool that returns secrets gets caught here.
  7. Feed results back — add the tool output to the conversation and loop back to step 1.

When the LLM finally responds without requesting tools, the harness runs post-guardrails, compliance rules, and the review gate on the final output. The result.iterations array gives you full observability into every step — what tools were called, which were blocked, how many tokens each iteration consumed.

// After the run, inspect what actually happened:
for (const iter of result.iterations) {
  console.log(`Iteration ${iter.iteration}:`, iter.final ? 'FINAL' : 'CONTINUE');
  for (const tc of iter.toolCalls) {
    console.log(`  ${tc.allowed ? '✅' : '🛑'} ${tc.tool}`, tc.blockedReason ?? '');
  }
  console.log(`  Tokens: ${iter.tokens}`);
}

In the security review example, the agent ran 4 iterations — listing files, reading source code, searching for secrets, then delivering a structured review. Each tool call was validated. The total cost, token count, and duration were tracked across the full loop, not just the final call. That’s the difference between “I called an LLM” and “I controlled an agent.”

Multi-Provider With Zero-Config Credentials

The library ships with providers for OpenAI, Anthropic, GitHub Models, and GitHub Copilot. The GitHub Models provider is model-agnostic — you can run openai/gpt-4o through it today, or swap to any model in the GitHub Models catalog. The credential resolver auto-discovers tokens from six sources (env vars, config files, gh CLI, OS keychain) so there’s no manual setup on developer machines.

Testing Behavior, Not Execution

The library has 220 tests across four tiers, but the eval tests taught me the most. Unit tests verify that code runs. Eval tests verify that guardrails actually block dangerous output and that escalation triggers on the right confidence thresholds. That distinction — testing behavior vs. testing execution — is exactly what separates a production harness from a weekend prototype. If you’re building agents and only testing happy paths, you’re missing the point. I’ve written about common mistakes when building custom agents before, and inadequate testing is always near the top.

What This Means for Engineers

If you’re running agents in production — or about to — here’s what I’d do:

  1. Inventory your agents. You probably have more than you think, especially if non-engineering teams have been building with low-code tools.
  2. Define governance boundaries. Which tools can agents call? What’s the cost ceiling? Who gets paged when confidence is low?
  3. Instrument observability at the iteration level. Knowing an agent “succeeded” isn’t enough. You need to see every tool call, every token spent, every guardrail check — per iteration.
  4. Start with golden paths. Give teams a blessed harness config and let them customize within those boundaries. It’s faster than auditing freeform agent deployments after the fact.

Some will argue this is over-engineering. The same was said about Kubernetes when people were happily running Docker on VMs. Your agent fleet needs the same discipline as your container fleet — the consequences of ungoverned autonomy are just harder to see until something goes wrong.

The Bottom Line

Agents without harnesses are prototypes. The harness is what turns “cool demo” into “production system” — the same way a container runtime turns an application into something you can actually deploy, monitor, and trust. The companies winning with AI in 2027 won’t be the ones with the most agents. They’ll be the ones with the best harnesses.

The full library is open source at github.com/htekdev/agent-harness. It’s ~2,000 lines of TypeScript, zero external SDK dependencies, and built to be forked. If you’re building agent infrastructure, I’d love to see what you do with it.

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