How coding agents actually work (non-technical)

If you manage developers but do not write code yourself, the rise of coding agents can feel opaque. Your team talks about “running agents” and “agent-generated PRs,” and the results show up in your repositories, but the mechanism behind it is unclear. This guide explains how coding agents actually work, without jargon, so you can make informed decisions about how your team uses them.

The simplest mental model

Think of a coding agent as an extremely fast but inexperienced junior developer who has read every programming book ever written but has never worked at your company before.

This junior developer can read your entire codebase, understand the patterns, write code that follows those patterns, and run tests to check their own work. They work quickly, do not get tired, and can handle multiple tasks in parallel. But they do not understand your business context, they sometimes make confident-sounding mistakes, and they need someone to review their work before it ships.

That mental model — a capable but unseasoned contributor who needs oversight — is the right starting point.

How agents “see”: reading context

Before an agent writes a single line of code, it reads. A lot.

When you give an agent a task like “fix the login bug on the settings page,” it first gathers context. It reads the relevant files in your codebase, looks at how similar code is structured elsewhere in the project, examines any related tests, and reads the task description you provided. Some agents also read documentation, pull request history, and coding style guides.

Think of this as the new hire spending their first day reading the codebase and the internal wiki. Except the agent does it in seconds rather than days. The result is a working understanding of your code — not perfect, not as deep as someone who has worked in it for years, but sufficient to make useful contributions.

The limitation: the reading window

Agents have a “context window” — a limit on how much they can hold in mind at once. Imagine trying to understand a large codebase but only being able to keep a certain number of pages in front of you at any time. The agent has to choose which files to read and which to set aside. For large codebases, this means agents sometimes miss relevant context that a human developer who has lived in the code would naturally remember.

How agents “think”: reasoning about code

Once an agent has read the relevant context, it reasons about what to do. This is where the AI model — the “brain” — comes in.

The agent’s thinking process looks something like this: “The user wants pagination on the blog page. I can see the blog page currently loads all posts at once. I need to add a page parameter to the URL, limit the query to N posts per page, and add navigation buttons. The existing project uses this particular UI component library, so I should use their pagination component.”

This reasoning is not rule-following or pattern-matching in the traditional programming sense. The agent generates a plan based on understanding the intent behind the request and the context of the codebase. It considers multiple approaches and selects one that fits the patterns it observed.

The limitation: hallucination

Sometimes the agent’s reasoning goes wrong. It might “remember” a function that does not exist, reference an API that works differently than it thinks, or generate code that looks correct but has a subtle logic error. This is called hallucination — the agent produces output that is plausible but incorrect.

Hallucination is not random. It tends to happen when the agent is working outside the patterns well-represented in its training or when the task requires knowledge it does not have. Recognizing this pattern helps your team know where to focus their review effort.

How agents “act”: making changes

After reasoning about the approach, the agent acts. It edits files, creates new ones, runs commands, executes tests, and checks results. If a test fails, it reads the error, adjusts its approach, and tries again. This loop — act, check, adjust — continues until the task is complete or the agent gets stuck.

The actions themselves are the same ones a human developer would take: editing source code, running the test suite, checking for linting errors. The difference is speed and tirelessness. An agent can try ten approaches to a bug fix in the time it takes a human to try one.

The limitation: judgment

Agents optimize for completing the task as described. They do not weigh business priorities, consider team morale, or think about whether the task should be done at all. If you ask an agent to “add a feature that tracks user behavior,” it will build the tracking without questioning whether it raises privacy concerns. The judgment about whether to build something remains entirely human.

What agents cannot do

Understanding what agents cannot do is as important as understanding what they can.

They cannot understand your business. An agent does not know that your largest customer is threatening to churn, that the sales team promised a feature by Friday, or that the compliance audit is next month. Business context that shapes priorities is invisible to agents.

They cannot assess their own confidence. A human developer says “I am not sure about this approach, let me check with the team.” An agent presents uncertain output with the same confidence as certain output. Your team needs to bring the skepticism.

They cannot replace architectural thinking. Agents work well at the task level — implement this feature, fix this bug, write this test. They do not make good architectural decisions because those require understanding the long-term trajectory of the product, the team’s capabilities, and trade-offs that span months of future development.

They cannot catch their own blindspots. If the context window missed a critical file, the agent does not know what it does not know. It will produce a solution that seems complete but misses an important dependency or interaction.

Why oversight matters

Every limitation above points to the same conclusion: agents need human oversight. Not because they are unreliable (they are remarkably capable), but because the gap between “code that works” and “code that should ship” includes judgment that only humans can provide.

The most effective teams treat agent output the same way they treat code from a new team member — review it, question the assumptions, verify it in context, and build trust gradually.

What this means for you as a leader

You do not need to understand the technical internals of large language models to manage a team that uses agents effectively. You need to understand the capabilities and limitations described above, set expectations for review rigor, and ensure your team has the visibility tools — like Dailybot — to track what agents are producing alongside human work.

The organizations getting the most value from agents are not the ones with the most advanced AI. They are the ones with the best oversight, the clearest processes for reviewing agent output, and the strongest culture of human judgment applied to machine-generated work. That is a leadership challenge, not a technical one.