Inside AI Coding Agents: Architecture, Tools, and Agentic Loops

AI coding agents have rapidly evolved from simple code completion tools into sophisticated autonomous systems capable of writing, testing, and debugging entire applications. Understanding how these agents work under the hood reveals fundamental patterns that extend far beyond coding—into synthetic media generation, content creation, and virtually every domain where AI operates autonomously.

The Core Architecture: More Than Just an LLM

At their foundation, AI coding agents combine a large language model with a carefully orchestrated system of tools, memory, and control loops. The LLM serves as the reasoning engine, but it's the surrounding infrastructure that transforms a chatbot into an agent capable of taking real-world actions.

The key insight is that modern agents don't simply generate text—they plan, execute, observe, and iterate. This agentic loop distinguishes them from traditional LLM applications and enables the kind of multi-step reasoning required for complex coding tasks.

Tool Calling: The Bridge to Action

The most fundamental capability enabling AI agents is tool calling—the mechanism by which an LLM can request specific actions in the external world. When an AI coding agent needs to read a file, run tests, or execute shell commands, it doesn't do so directly. Instead, it generates a structured request that the surrounding system interprets and executes.

Modern tool-calling implementations typically use JSON-formatted function calls embedded in the model's output. The agent might output something like:

{"tool": "read_file", "path": "src/main.py"}

The orchestration layer parses this request, executes the actual file read, and feeds the results back to the model for continued reasoning. This pattern—generate request, execute externally, return observation—forms the basic rhythm of agent operation.

Tool Definitions and Schemas

Agents work from a predefined toolkit, with each tool described via structured schemas that specify parameters, expected inputs, and return types. This allows the LLM to understand what capabilities are available and how to invoke them correctly. The quality of these tool definitions significantly impacts agent performance—ambiguous or poorly documented tools lead to failed invocations and wasted compute.

The Agentic Loop: Plan, Act, Observe, Reflect

The agentic loop is the control structure that enables autonomous operation. Unlike single-shot inference, agents operate in cycles:

1. Plan: The agent analyzes the current state and determines the next action needed to achieve its goal.

2. Act: It generates a tool call or response based on its plan.

3. Observe: The system executes the action and returns the results to the agent.

4. Reflect: The agent incorporates the new information and updates its understanding of the problem.

This loop continues until the agent determines the task is complete or encounters an irrecoverable error. The sophistication of this loop—particularly the planning and reflection stages—varies significantly between implementations.

Memory Architectures: Maintaining Context

One of the most challenging aspects of agent design is memory management. LLMs have finite context windows, but real coding tasks often involve large codebases and long interaction histories. Agents employ several strategies to handle this limitation:

Conversation memory: The most basic approach appends all interactions to a growing context. This works for short tasks but quickly exceeds token limits.

Summarization: Agents can compress previous interactions into summaries, preserving key information while reducing token count.

Retrieval-augmented memory: More sophisticated systems store interaction history in vector databases, retrieving relevant past context on demand rather than including everything.

Working memory: Some architectures maintain a structured "scratchpad" where agents can store and retrieve key facts and intermediate results.

Planning and Decomposition

Advanced coding agents don't tackle complex tasks in a single pass. Instead, they employ task decomposition—breaking large objectives into smaller, manageable subtasks. This hierarchical planning mirrors how human developers approach problems.

Some agents use explicit planning phases where they generate step-by-step plans before execution. Others employ more implicit reasoning, adjusting their approach dynamically based on feedback. The tradeoff involves upfront planning cost versus adaptability to unexpected situations.

Implications for Synthetic Media and Beyond

The architectural patterns powering coding agents—tool calling, agentic loops, memory systems, and planning—are increasingly appearing in synthetic media generation tools. Video generation agents can similarly plan shot sequences, invoke rendering tools, observe outputs, and iterate toward a final product.

Understanding these mechanisms matters for digital authenticity: as AI agents become more capable of autonomous content creation, the systems that detect and verify synthetic content must account for the sophisticated, iterative processes that created them.

The rise of agentic AI represents a fundamental shift from AI as a tool to AI as an autonomous actor. Whether generating code, video, or other media, the underlying patterns of plan-act-observe-reflect define the new frontier of artificial intelligence.

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