How AI Coding Agents Work: A Deep Dive into Copilot, Cline, Kiro, and Antigravity
For a long time, “AI-assisted coding” meant one thing: autocomplete.You wrote a few lines, the model predicted the next tokens, and you pressed Tab. That era is over. Modern AI coding agents can read repositories, plan multi-step changes, execute com...
For a long time, “AI-assisted coding” meant one thing: autocomplete.
You wrote a few lines, the model predicted the next tokens, and you pressed Tab.
That era is over.
Modern AI coding agents can read repositories, plan multi-step changes, execute commands, interpret failures, and iteratively correct themselves. They do not merely suggest code—they act on intent.
This article explains how AI coding agents work internally, independent of any specific tool, by looking at the architectural patterns common across today’s most capable systems.
The Shift from Suggestions to Agency
Earlier tools answered a narrow question:
“What code should come next?”
Agents answer a broader one:
“What sequence of actions will achieve the desired outcome?”
To do this, they must reason, plan, execute, and adapt—capabilities that require more than language generation.
The Core Agent Loop
Every AI coding agent operates around a continuous decision loop. This loop is the foundation of agentic behavior and explains why these tools feel fundamentally different from chat-based assistants.
The agent begins by understanding the goal, gathers relevant context from the codebase, forms a plan, executes actions such as editing files or running commands, and observes the outcome. Failures feed back into the planning phase until the task is resolved.
This loop continues autonomously but within defined safety boundaries.
Why Large Language Models Are Only One Part of the System
The large language model is the reasoning engine, but it is not the agent itself.
On its own, an LLM cannot read files, modify repositories, or run code. The agent framework provides controlled access to tools that allow the model to interact with the real system.
This separation is intentional. The model reasons; the agent acts. Together, they form a complete system capable of meaningful software development work.
Context Is the Hidden Engine
The quality of an agent’s output depends more on what it sees than on how smart the model is.
Modern agents dynamically load relevant files, summarize large modules, track recent changes, and maintain short-term working memory. Poor context leads to shallow fixes. Strong context leads to architectural awareness.
From a systems perspective, context management is often the most complex and expensive part of an AI coding agent.
Execution Turns Suggestions into Reality
Execution is what distinguishes agents from assistants.
When an agent runs a test suite or executes a build command, it encounters objective reality. Errors, stack traces, and failing assertions provide concrete feedback that cannot be ignored.
This creates a feedback loop identical to human development workflows:
Agents that can execute and observe outcomes are forced to correct themselves. This dramatically reduces hallucinations and increases reliability.
Autonomy Exists on a Spectrum
Not all agents operate with the same level of independence. Some prioritize safety and speed, while others emphasize autonomy and depth.
At lower autonomy levels, the agent proposes changes and waits for human approval. At higher levels, it executes multiple steps independently, surfacing results only when confidence is high or intervention is required.
This spectrum is a design choice, not a limitation.
Why Multi-Agent Architectures Are Emerging
Advanced systems increasingly split responsibilities across multiple specialized agents. One agent plans, another implements, another reviews, and another validates through tests.
This mirrors real engineering teams and improves correctness by introducing internal checks and balances.
This approach reduces error accumulation and enables longer, more complex task execution.
Safety and Trust Boundaries
Because agents can modify real systems, guardrails are essential. Common mechanisms include explicit user approvals, sandboxed execution environments, command auditing, and resource limits.
Trust is built gradually. The more autonomous the agent, the more important these controls become.
What This Means for Software Engineers
AI coding agents shift the engineer’s role upward. Less time is spent writing boilerplate or debugging trivial issues. More time is spent on defining intent, reviewing architectural decisions, and validating outcomes.
Engineers who understand how these agents work internally will be better equipped to guide them effectively—and to recognise when human judgment is irreplaceable.
Final Thoughts
AI coding agents are not simply better autocomplete tools. They are systems that combine reasoning, context, execution, and feedback into a continuous loop.
Understanding this architecture is quickly becoming a core software engineering skill.
The future is not about writing more code faster.
It is about designing better systems with intelligent agents as collaborators.