From Prompt to Loop Engineering, a simple guide for the non-coders and knowledge workers

The baseline

What it is

An AI model can't read our minds (yet): it only works from the exact words we give it. That's precisely where prompt engineering comes in: the craft of writing good instructions for it.

The more deliberately we frame a request, the more reliable the answer becomes.

How it works

To start engineering a prompt, give the AI model a role to play, a clear task to perform, the context it needs, the constraints to respect, and the format the answer should take, as the two versions below show.

Do that, and everything the model needs to answer your query fits inside that single message, with no outside help required.

Example

Limitations

This is where prompt engineering hits its ceiling. On its own, the AI model can't know anything that happened after its knowledge cutoff, and for more advanced or exploratory work, you can't fit everything it needs into a single prompt.

That's where context engineering comes in.

Increasing the limits

What it is

The prompt we engineer, or don't, is only one part of what fills the AI model's context window.

Think of it as the model's working memory: the conversation so far, its instructions, our preferences, the documents we've added, and every search or tool call it has made all share that same space.

How it works

The context window, although generous, is fixed in size. Every piece that makes it up is competing for the same limited room, and the AI model's performance degrades the more that window fills up, as the breakdown below shows.

You might ask: if we give too little context, does the quality drop? And if we fill the context window too much, does it drop too?

The answer is yes, on both counts. That's exactly what context engineering is: the craft of providing quality, fit-for-purpose context.

Example

Ask the same question twice, once without extra context and once with it:

A context engineering checklist, do versus don't:

• Do: write a clear prompt. Use prompt engineering to define the task, expected result, audience and constraints. Don't: combine unrelated tasks, or leave the desired outcome open to interpretation.
• Do: share relevant data. Provide the facts, documents, examples or previous work the model needs. Don't: dump everything into the context, or include outdated and contradictory information.
• Do: add useful links. Point to reliable sources and explain what the model should extract from them. Don't: share links without saying why they matter, or assume the model can open them.
• Do: connect the right tools. Give the model access to the systems it needs to search, calculate, retrieve or act. Don't: provide unnecessary tools, or leave it unclear when and how to use them.
• Do: remove unnecessary context. Keep only what helps the model complete the task accurately. Don't: mistake more context for better context.

Limitations

Context engineering improves results, but it can't guarantee accuracy. Without a plan that includes an adversarial review, the AI model can still misunderstand instructions, overlook relevant details, or rely on incorrect information.

Whenever we compensate for that with a longer prompt, more context and detailed task planning, the context window fills up.

Eventually we either hit that ceiling or the AI model's output quality drops significantly. That's exactly why harness engineering matters.

Managing from the outside

What it is

Instead of cramming more context, internet searches and tool calls into the model's context window, AI products can use harnesses to manage all of that around the model.

An AI harness is software that gives a model structured access to context, tools, memory and feedback, while guiding how it completes a task. Claude Code, Cursor and GitHub Copilot are examples within coding environments.

A harness can keep the plan, task list and progress outside the model's active context. It gives the model one step at a time, with only the relevant information, records the result, and then prepares the next step.

This reduces context overload, but completing each step still requires a process for evaluating the result and deciding what happens next: a loop.

For example, a plan might look like this, with the assistant holding only the step it is currently on:

How it works

As an end user, you practice harness engineering by configuring how the AI works: provide reusable instructions, connect only the tools and data it needs, and define checkpoints for review or approval.

Don't grant unnecessary access, automate unclear processes, or allow important actions without verification.

The goal is to build a reliable workflow around the model, not simply give it a larger prompt.

Example

Imagine asking a workplace AI assistant to prepare a brief for an upcoming client meeting. The same task, engineered at three levels:

The user has engineered the harness by controlling what the AI can access, what it can do, how its work is verified, and where human approval is required.

Limitations

A harness gives the model tools, rules and checkpoints, but it can't guarantee the first attempt succeeds.

Complex tasks often need the AI to inspect its own output, compare it with the goal, and adjust its approach.

That need for repeated action, feedback and improvement is exactly what leads to loop engineering.

Automating the thinking

What it is

A harness provides the environment for completing a task. Loop engineering adds repetition: the system performs the task, evaluates the result, and decides whether to finish, try again, or involve a person.

A loop can run once or repeatedly. Each run begins with a trigger, works through the harness, checks the result against a goal, and follows a defined next step.

How it starts

A loop can start in three common ways:

The goal does not start the loop. It determines when the loop should stop.

How it works

As an end user, you practice loop engineering by defining the trigger, success criteria, retry limits, and points requiring human approval.

An unattended loop also needs:

• Workspaces: isolated environments that protect human work.
• Skills: reusable methods for recurring tasks.
• Connections: controlled access to files, tools and communication channels.
• State: a record of completed work, pending actions and previous attempts.
• Workers: temporary agents that can handle separate parts of a larger task.

Do not launch a loop with a vague goal, skip output checks, or give it unrestricted access to sensitive systems. The goal is a workflow that can evaluate and correct its output safely, not one that repeats the same mistake faster.

A loop can also coordinate several workers in parallel, collect their results, and decide what happens next. This is multi-agent orchestration rather than recursion.

Example

Picture the client brief from harness engineering again, but now nobody has to ask for it:

The team stops preparing the brief and starts reviewing it instead.

Limitations

A loop amplifies the system underneath it. A vague goal, weak harness or unreliable evaluation will fail repeatedly while consuming more time and tokens.

It can also become stuck, repeatedly changing its output without moving closer to the goal, or produce work faster than people can meaningfully review it.

Clear stopping conditions, retry limits and human approval remain essential for sensitive or difficult-to-reverse actions.

What every loop needs

We have now met several loops in this guide: an assistant that searches until it has enough, a harness that works its way through a task list, and an operation that drafts a client brief on its own every Monday.

They do very different jobs, yet every dependable loop is built from the same five parts, shown here as a single running cycle. Miss one of them, and the loop starts to misbehave.

It starts with a trigger, the reason each run begins, one of the three kinds we just saw. Without one, the loop just sits there waiting for us to ask, which is exactly the dependence loop engineering set out to remove.

It also needs a goal, a target and a clear definition of done: send the weekly summary, make the tests pass, answer the question. Without one, it can't tell progress from motion, and it never quite knows when to stop.

Then it needs feedback: after each pass, the loop compares what it produced against that goal. This single choice matters more than any other. A closed loop checks its own result and corrects course, like a thermostat that keeps reading the room and nudging the heating until it reaches the temperature we set; it is self-correcting, and it is the only kind we'd trust with real work.

An open loop, by contrast, follows a fixed plan and never looks back, like a sprinkler on a timer that waters the garden whether or not it rained overnight. Open loops are simpler, but they drift the moment reality stops matching the plan.

Next, a stop condition, a rule for when to end: the goal is reached, a budget of time or attempts is spent, progress has stalled, or a person calls a halt. This is the brake, and we already know why it matters: a loop that can't stop itself is exactly the one that runs away.

Most of the worry around these systems really comes down to this: a loop with no brake.

Last, guardrails. Because the loop runs while we're not watching, we decide up front what it may touch, how far it may reach, and which actions need a human to approve them first. If the goal says where the loop is heading, the guardrails say where it must never go.