Harness Engineering vs Loop Engineering

Loop engineering refers to the practice of designing agentic workflows that guide AI agents toward completing user-defined goals with minimal human intervention. Instead of requiring human prompting at every step, agent loops enable agents to dynamically act, observe, make decisions and iterate until a task is complete.

An agentic loop usually comprises of the following stages: Goal, Action, Observation and Adjustment.

1. Goal

A recursive goal is evaluated at every iteration of an agentic loop to help keep the agent on task, preventing unnecessary iterations and managing token costs. These goals should be specific and include clear and verifiable stopping criteria.

2. Action

This is what the agent does to move closer towards the end goal, and can be writing code, fixing a bug etc.

3. Observation

The agentic system then evaluates the result of the action.

4. Adjustment

Based on the observation, the system evaluates the feedback and makes any necessary changes to its approach before restarting the agentic loop.

Poorly built loops are inefficient and can lead to unnecessary iterations, resulting in wasted tokens or incorrect reasoning. To design agentic loops well, the following components are usually included:

• Automations/Scheduling

Repetition is what separates loops from a one-time prompt. Both automations and scheduling determine the cadence of the loop, telling it what to do and how often to do it. This can be done through Github Actions, or a time-based scheduler (cron job).

• Hooks

Unlike scheduled automations, hooks are instructions triggered by events such as generating code, editing a file, calling a tool or completing a task. Hooks can occur before or after the associated event. Developers largely use hooks for security and quality, such as to enforce policies, validate outputs and trigger workflows automatically. Hooks remove tasks such as governance and quality checks from the agents in the loop, reducing token costs and compute use.

• Context engineering

Every cycle of the loop generates data (context) that are used in subsequent loops. Although context windows are getting larger, providing too much contextual information can dilute the relevancy of information generated, increase costs and make it harder for the model to identify the most important information.

• Tool access

Without tool access, agents can only describe what they want to do rather than act on it. MCP servers (model context protocol) are commonly used to allow agents to carry out autonomous actions with connectors and tools.

• Worktrees

Worktrees enable branching so that multiple agents can work in parallel without affecting each other’s work. Git worktrees allow multiple working directories to share a single repository, enabling parallel branches without duplicating repository history.

• Skills

Skills contain task-specific project knowledge for a single recurring workflow. Agents reference this skill file when carrying out associated tasks. These skills can be shared across projects and repositories as plug ins. Without them, users need to include project context with each session or agents would have to guess what they are supposed to do

• Subagents

Specialized agents can be delegated by the primary agents to fulfil a specified role, such as research, exploration etc. Good loop engineering practices typically use a maker/checker structure with one agent checking another’s code for improved code quality.

• Spine

This is a persistent state/memory to track project progress and prevent error repetition. Every cycle, the outcomes of agents’ actions are added to the spine and the spine is used to maintain state ad context to inform future iterations.

Importance of human-in-the-loop

Even with checker agents, the responsibility of code that is shipped to production still rests on human developers. AI governance requires human oversight to verify the functionality of code and ensure that there is no exposure of sensitive data or a breach of regulations.

Harness Engineering

Harness engineering refers to the discipline of designing the systems, constraints and feedback loops that wrap around an AI model to make it reliable in production. The agent harness is basically everything except the model: the tools, memory, guardrails, verification and orchestration.

Agent = Model + Harness

While the model provides the raw reasoning, the harness transforms that capability into a reliable, auditable system.

Why Models Need a Harness in the First Place

A raw language model, on its own, is limited in specific and predictable ways. It cannot:

• Hold durable state across sessions as every inference call starts blank
• Execute code or take real-world action on its own
• Access real-time knowledge beyond its training cutoff
• Reliably self-verify its own output; confident-sounding answers aren’t the same as correct ones

These aren’t flaws to be prompted away; they’re structural gaps that only get closed by the system wrapped around the model. A harness converts problems the model handles poorly (like remembering) into problems it handles well (like reading from an external store).

The Core Components of a Harness

• Filesystems

The most foundational primitive. They give agents a workspace to read and write data, offload work that doesn’t fit in the context window, persist state across sessions, and act as a shared surface for multiple agents (or humans) to collaborate through.

• Bash / code execution as a general tool

Rather than pre-building a tool for every possible action, harnesses increasingly just give the model a computer and let it write and run code to solve problems on the fly.

• Sandboxes

Isolated, disposable environments to execute that code safely, with sensible default tooling (language runtimes, git, test runners, browsers) so agents can act, observe, and verify without risking the host system.

• Memory and search

Since models can’t edit their own weights, “learning” happens through context injection: standards like AGENTS.md persist knowledge across sessions, while web search and MCP tools like Context7 fill in anything past the training cutoff.

• Skills

Reusable, task-specific instruction files that convert improvised, regenerate-from-scratch workflows into consistent, guided execution — and can be loaded progressively so they don’t clutter the context window unnecessarily.

• Orchestration and subagents

Delegating isolated subtasks to specialized subagents keeps the main reasoning thread clean, prevents intermediate noise from polluting context, and is one of the most effective tools for staying coherent on long-horizon work.

• Guardrails and verification

Permission boundaries, schema validation, deterministic test suites, and secondary “checker” models that gate a step’s output before it’s trusted.

Fighting Context Rot

Context management is one of the responsibilities of a harness. As a context window fills up, model performance degrades. It loses track of constraints, drifts from the original goal, and produces weaker output. Harnesses counter this through:

• Compaction

Intelligently summarizing and offloading context before the window fills up

• Tool call offloading

Keeping only the head and tail of large tool outputs in context, with the full result parked in the filesystem

• Progressive disclosure

Loading skills, tools, or memory only when they’re relevant, instead of front-loading everything at session start

Classifying Failures to Fix the Right Layer

When an agent fails, the fix depends on which layer broke.

• Context failure — the agent didn’t have the right information at the right time (fix: retrieval or memory)
• Constraint failure — the agent had the information but did something out of scope (fix: a guardrail)
• Verification failure — plausible-looking but wrong output slipped through (fix: a test suite or reviewer step)
• Planning failure — the agent took the wrong overall approach (fix: better orchestration or task decomposition)

The bottom line that I learnt: the model provides the intelligence, but the harness is what makes that intelligence usable, reliable, and auditable in production.