How AI Agents Work: Architecture, LLMs, and the Agent Loop

An AI agent works by running a continuous loop: it perceives its environment, reasons about what to do next using a large language model, takes action through tools, and learns from the result. That loop -- perceive, reason, act, learn -- is the fundamental mechanism behind every AI agent in production today, from a simple task runner to a multi-agent orchestration system coordinating twenty specialists.

If you have ever wondered how an AI agent goes from receiving a goal like "refactor this module and deploy it" to actually doing it -- reading files, writing code, running tests, fixing failures, and shipping the result -- the answer is not magic. It is architecture. Specifically, it is the interplay between a reasoning engine (the LLM), a tool interface (how the agent interacts with the world), a memory system (how the agent retains context), and a planning mechanism (how the agent decomposes goals into steps).

This guide is a technical walkthrough of how AI agents work, grounded in real examples from Nevo -- a self-improving AI agent system running in production. If you are new to AI agents entirely, start with What Are AI Agents?. For the full taxonomy, read Types of AI Agents. This post sits between: deeper than the overview, narrower than the classification.

Table of Contents

1. The Agent Loop: Perceive, Reason, Act, Learn
2. The LLM as Reasoning Engine
3. Tool Use: How Agents Interact with the World
4. Memory: Short-Term, Long-Term, and Episodic
5. Planning and Goal Decomposition
6. The Quality Problem: Why Loops Alone Are Not Enough
7. Multi-Agent Architecture
8. Self-Improvement: The Learning Feedback Loop
9. A Complete Agent Execution: Step by Step
10. FAQ

The Agent Loop: Perceive, Reason, Act, Learn

The agent loop is the core execution cycle that every AI agent runs. It is sometimes called the perceive-reason-act loop, the sense-think-act loop, or simply the agent cycle. The names vary. The structure does not.

Here is how it works:

Perceive. The agent gathers information about its current environment. For a coding agent, this means reading source files, checking error logs, inspecting test results, scanning documentation, or reviewing the output of a previous action. For a business agent, it might mean reading emails, pulling database records, or monitoring a dashboard. The agent does not operate blind -- it collects context before every decision.

Reason. With context in hand, the agent's LLM processes the information and decides what to do next. This is not simple pattern matching. Modern LLMs perform multi-step reasoning: they evaluate the current state against the goal, consider multiple possible actions, weigh trade-offs, and select the approach most likely to make progress. The quality of this reasoning is what separates a capable agent from one that flails.

Act. The agent executes its chosen action by calling a tool. It might write a file, run a shell command, make an API call, search the web, or send a message. The action produces a result -- new output, an error, a changed file, a response from an external service -- which feeds back into the loop.

Learn. The agent observes the result of its action and incorporates that feedback. Did the test pass? Did the API return an error? Did the file write succeed? This observation becomes new perception, and the loop restarts. In more sophisticated systems, learning also means encoding lessons permanently -- turning a mistake into a rule that prevents recurrence.

The loop runs continuously until the goal is met, progress stalls, or human input is needed. A simple task might complete in three iterations. A complex feature might take hundreds, each cycle building on everything before it.

What makes this powerful is compounding. Each iteration starts with more context than the last. The agent builds toward the goal incrementally, course-correcting along the way. This is fundamentally different from a chatbot, which treats each message as a largely independent interaction.

The LLM as Reasoning Engine

The large language model is the brain of an AI agent. It is not the agent itself -- the agent is the full system of loop, tools, memory, and planning. But the LLM is what gives the agent the ability to understand natural language goals, reason about complex situations, and decide what action to take next.

An LLM is agent-ready when it can do four things reliably:

Goal decomposition. Given a high-level objective like "add authentication to this API," the model breaks it into ordered steps: check existing patterns, choose an approach, implement, test, document. This requires understanding both the goal and the domain well enough to produce a sensible plan.

Structured tool calling. The model must generate precise, syntactically correct function calls -- typically as JSON -- that an external runtime can execute. If the model hallucinates parameter names or invents tools that do not exist, the agent loop breaks immediately. Reliable tool calling is the single most important capability for agent work.

Long-context coherence. Agent tasks involve dozens or hundreds of reasoning steps, each building on previous results. The model must maintain coherent understanding across a long and growing context window without losing track of earlier decisions or the overall goal.

Error recovery. Real-world tool calls fail. An agent-ready model does not just report the error and stop. It reasons about what went wrong, adjusts its approach, and retries with a different strategy.

The choice of LLM has an outsized impact on agent performance. A model with a massive context window may degrade in attention quality at position 200K. A strong text generator may still be poor at structured tool calling. The models that work best for agents in 2026 -- Claude Opus, GPT-4o, Gemini 2.5 Pro -- excel across all four capabilities simultaneously. For a detailed comparison, see LLM AI Agents: How Language Models Power Autonomous AI.

Nevo uses Claude as its primary reasoning engine, with model routing across three intelligence tiers. Simple tasks like type checking route to Haiku (fast, cheap). Standard development routes to Sonnet. Complex reasoning -- code review, architecture decisions, root cause analysis -- routes to Opus. The right intelligence for each task, rather than burning top-tier capacity where it is not needed.

Tool Use: How Agents Interact with the World

Without tools, an LLM is a brain in a jar. It can think, but it cannot do. Tools are what give an AI agent hands.

Tool use is the mechanism by which an agent converts a reasoning decision into a real-world action. The LLM generates a structured function call -- a tool name and a set of arguments -- and the agent runtime executes that call against the actual environment. The result flows back to the LLM as new context, and the loop continues.

What Counts as a Tool

Anything the agent can invoke counts as a tool. In modern agent systems, the tool surface includes:

• File system operations -- reading, writing, editing, and searching files
• Shell commands -- running build scripts, tests, linters, deployment commands
• API calls -- interacting with external services, databases, cloud providers
• Web search -- finding documentation, researching solutions, gathering current information
• Browser automation -- navigating web pages, filling forms, extracting data
• Code execution -- running Python scripts, JavaScript, SQL queries
• Communication -- sending messages via Telegram, Slack, email

The breadth of available tools directly determines the breadth of tasks an agent can handle. File system plus shell equals software development. Add web search and it can do research. Add browser automation and it can interact with any web application. Add communication tools and it can collaborate with humans.

The Model Context Protocol (MCP)

The Model Context Protocol is an open standard that solves a fragmentation problem. Before MCP, every agent system implemented its own tool interface, forcing developers to build custom integrations for each tool-platform combination.

MCP defines a universal protocol: a tool developer builds one MCP server, and any MCP-compatible agent can use it. The result is an expanding ecosystem of pre-built integrations -- database connectors, cloud service APIs, development tools, monitoring systems -- that any agent can plug into. For a deeper exploration, see What Is MCP?.

Tool Calling in Practice

Here is what a tool call looks like in the agent loop. The agent is tasked with fixing a failing test:

1. Perceive: The agent reads the test output and sees a failure in user_auth_test.py at line 47 -- an assertion error because the response status code is 401 instead of 200.
2. Reason: The LLM analyzes the error, identifies that the authentication middleware is rejecting the request, and decides it needs to read the auth configuration file.
3. Act: The agent calls the read_file tool with the path to the auth config. The runtime executes the read and returns the file contents.
4. Perceive (again): The agent reads the config and discovers that the test environment is missing a required API key variable.
5. Reason: The LLM decides to update the test setup to include the missing environment variable.
6. Act: The agent calls the edit_file tool to add the variable to the test configuration.
7. Act (again): The agent calls the run_command tool to re-run the test suite.
8. Perceive: The test passes. The goal is met.

Eight steps. Four tool calls. Zero human intervention. That is the agent loop in action, with tool use as the mechanism that turns reasoning into results.

Memory: Short-Term, Long-Term, and Episodic

Memory is what separates an AI agent from a stateless function call. Without memory, every interaction starts from zero. With memory, the agent accumulates knowledge, builds context, and gets better over time.

AI agent memory operates on three timescales:

Short-Term Memory (Context Window)

Short-term memory is the conversation context -- everything the LLM can see in a single session. This includes the system prompt, the user's goal, the results of every tool call, and the agent's own reasoning up to the current point. It is bounded by the LLM's context window, which ranges from 128K to 10 million tokens in 2026.

Short-term memory is fast and precise. The agent has direct access to everything in the context. But it is ephemeral -- when the session ends, the context vanishes. And it is finite -- once the context window fills, the agent either loses early information or must summarize and compress.

Long-Term Memory (Persistent Knowledge)

Long-term memory is information that persists across sessions. User preferences. Codebase patterns. Project-specific configuration. Learned procedures. This is stored in files, databases, or vector stores that the agent can query when it needs historical context.

The challenge with long-term memory is retrieval. Storing everything is easy. Knowing which memories are relevant to the current task is hard. The best systems use a combination of keyword search, semantic similarity (vector embeddings), and structured retrieval to surface the right context at the right time.

Nevo's long-term memory uses a three-stage pipeline inspired by how biological memory works: a sensory buffer captures raw session data, a hippocampal encoding stage extracts and consolidates facts, and a neocortical store holds permanent knowledge that informs future sessions. This means Nevo does not just remember what happened -- it distills what happened into what matters.

Episodic Memory (Experience Traces)

Episodic memory records specific past experiences -- not just facts, but the sequence of actions, decisions, and outcomes from previous tasks. If the agent failed at a task three sessions ago and eventually found a workaround, episodic memory lets it skip straight to the working solution instead of repeating the same failures.

In Nevo's architecture, this takes the form of session narratives that are processed, distilled, and stored in a searchable memory system using BM25 keyword search and embedding-based semantic search. The interplay between all three memory types is what gives advanced agents institutional knowledge -- not just individual session intelligence.

Planning and Goal Decomposition

The difference between a capable AI agent and a brittle one often comes down to planning. A well-planned approach to a complex task succeeds more reliably than an agent that tries to solve everything in one shot.

Planning is the process by which an agent takes a high-level goal and breaks it into a sequence of concrete, executable steps. This is sometimes called task decomposition, hierarchical planning, or goal decomposition. The core idea is the same: complex goals must be reduced to manageable pieces before execution begins.

How Planning Works in Practice

When an agent receives a goal like "build a REST API for user management," a good planning phase produces an ordered sequence: read the codebase, design the data model, create the migration, implement CRUD endpoints, add auth middleware, write tests, update docs, run the full suite. Each step has clear inputs, outputs, and success criteria. If step 4 fails, the agent debugs it in isolation without redoing steps 1 through 3.

Structured Planning Frameworks

The best agent systems formalize planning into templates rather than letting the LLM plan ad hoc each time. Nevo uses a PRD (Product Requirements Document) framework for any task touching three or more components. The framework decomposes work into stories with explicit dependencies, file scopes, and acceptance criteria -- each sized to fit a single context window. Stories without dependency conflicts run in parallel across isolated workspaces. Dependent stories execute sequentially.

This eliminates the most common planning failure: diving into implementation without understanding scope, then discovering halfway through that the approach conflicts with unexplored parts of the codebase.

Replanning

No plan survives reality unchanged. The best agents treat the plan as a living document. Each completed step produces new information that may change the optimal path forward. If the agent discovers the database does not support the assumed schema, it revises all downstream steps accordingly.

The Quality Problem: Why Loops Alone Are Not Enough

Here is the uncomfortable truth about the basic agent loop: it is not enough. A naive perceive-reason-act cycle will produce output, but there is no guarantee that the output is correct, robust, or production-ready. The loop generates. It does not verify.

This is why most early AI agents required constant human supervision. The agent would write code that compiled but missed edge cases. It would produce output that looked right but contained subtle errors that only emerged later.

Quality Gates

The solution is quality verification embedded directly into the architecture. Instead of a simple loop that runs until the goal appears met, the system runs output through independent checks before declaring success:

1. Type checking -- Does the code pass static analysis?
2. Testing -- Do the tests pass? Are there tests for the new code?
3. Linting -- Does the code follow style and convention rules?
4. Code review -- Does an independent reviewer find issues?
5. Refinement -- Are the reviewer's findings addressed?
6. Escalation -- If the code fails review after multiple attempts, does a fresh reviewer get involved?

Each stage is a gate. If the output fails a gate, it loops back for revision. Only output that clears every gate is considered complete.

Nevo implements this as an 8-stage mandatory pipeline: WRITE, TYPECHECK, TEST, LINT, CRITIQUE, REFINE, ESCALATE, ARBITER. If code fails after three refinement iterations, an escalation chain brings in fresh reviewers who have not seen previous attempts -- eliminating the risk of an agent stuck in a loop making the same mistakes.

The quality pipeline adds latency and cost. But it eliminates the most dangerous failure mode in AI: confident but wrong.

Multi-Agent Architecture

A single agent can handle many tasks. But for complex work, a multi-agent architecture -- where specialized agents each handle a specific aspect -- consistently outperforms a generalist trying to do everything.

The reasoning is practical: specialization works. A type checking specialist catches errors a generalist misses. A security reviewer identifies vulnerabilities that a feature-focused developer overlooks. Multiple agents also enable parallelism -- when subtasks have no dependencies, agents can work simultaneously in isolated workspaces, preventing cross-contamination where changes for one task break another.

The orchestrator is the agent that manages the others. It receives the goal, plans the work, dispatches tasks to specialists, handles failures, and assembles results. In a well-designed system, the orchestrator manages but does not implement. For a detailed look at multi-agent architectures in production, see AI Agent Systems.

Nevo operates this way with 21 specialized agents. The orchestrator decomposes projects via the PRD framework, routes stories to execution agents, runs completed work through the quality pipeline's specialists (typechecker, test-runner, linter, code-critic, fresh-reviewer, quality-arbiter), and merges verified results.

Self-Improvement: The Learning Feedback Loop

The most advanced AI agents do not just execute tasks -- they get better at executing tasks over time. This takes several concrete forms.

Error-to-Rule Pipelines

When an agent makes a mistake, it can either forget about it or encode a preventive rule that ensures recurrence is impossible. An incident monitor detects the failure. An incident analyst traces the root cause. The analyst generates a concise rule -- one to three sentences -- that prevents the root cause from recurring. The rule is encoded into the agent's operating instructions.

Nevo runs this pipeline in production. Dozens of operational rules have been generated from real incidents. Each one makes the system marginally more reliable, and those marginal improvements compound.

Skill Acquisition

Agents can proactively acquire new capabilities. When an agent identifies a gap -- a task domain it cannot handle, a tool it does not know -- it can research the solution, build the capability, and add it to its permanent toolkit. Nevo's Skill Forge does this autonomously: detect gap, research approaches, generate skill, validate, deploy.

Prompt Optimization

As agents accumulate execution traces, those traces can optimize the prompts that drive behavior. Techniques like DSPy's MIPROv2 analyze traces to find prompt formulations that produce better results, then update the agent's prompts accordingly. Learning at the meta-level: not improving what the agent does, but improving how well it reasons about what to do.

For a deeper exploration of these mechanisms, see How Nevo Learns: Self-Improvement in Practice.

Putting It All Together: An End-to-End Execution

Here is what happens when an AI agent receives the goal: "Add rate limiting to the API and deploy it to staging."

Planning. The orchestrator decomposes the goal into six stories: research approaches, implement middleware, add configuration, write tests, update documentation, deploy.

Perception and reasoning. The execution agent reads the codebase -- framework, existing middleware patterns, config structure. It searches the web for current best practices. Based on what it finds, it selects a token bucket algorithm with per-route configuration.

Action. The agent writes the middleware, adds configuration entries, updates route definitions.

Quality gate. The code enters the 8-stage pipeline. Type checking, tests, linting, code review. The critic flags a missing fallback for when the configuration store is unavailable. The agent adds fallback behavior, resubmits. The updated code passes all gates. The arbiter approves.

Deployment. The agent runs the staging deployment script, monitors output, verifies rate limiting is active.

Learning. The missed edge case is recorded. If the pattern recurs, it becomes a rule: "When implementing middleware that depends on configuration, always include a fallback for configuration unavailability."

Nine phases. Dozens of agent loop iterations. Multiple specialists. Zero human intervention. That is how AI agents work -- not as a single clever response, but as an architecture of compounding loops, verification gates, and persistent learning.

FAQ

What is the agent loop?

The agent loop is the core execution cycle of an AI agent. It consists of four stages: perceive (gather information from the environment), reason (use the LLM to decide what to do), act (execute a tool call), and learn (incorporate the result). The loop runs continuously until the agent's goal is achieved or human input is required.

How do AI agents use LLMs?

AI agents use large language models as their reasoning engine. The LLM processes the current context -- including the goal, environment state, and results of previous actions -- and generates the next action. This typically takes the form of a structured tool call that the agent runtime executes. The LLM is the brain; the tools are the hands.

What is the difference between an AI agent and a chatbot?

A chatbot responds to individual messages. An AI agent pursues goals. A chatbot waits for your next prompt. An agent plans its own work, executes multi-step tasks autonomously, uses tools to interact with the real world, and learns from the results. For a full comparison, see AI Agent vs Chatbot.

How do AI agents remember things between sessions?

Through multiple memory systems. Short-term memory is the LLM's context window within a single session. Long-term memory uses persistent storage -- files, databases, or vector stores -- to retain information across sessions. The best systems use structured retrieval to surface relevant memories when needed.

Can AI agents improve over time?

Yes. Error-to-rule pipelines encode mistakes as preventive rules. Skill forges generate new capabilities. Prompt optimization refines reasoning quality. These mechanisms cause performance to compound over time. For a real-world example, see How Nevo Learns.

Why do AI agents sometimes fail?

The most common failure mode is compounding errors -- a small mistake early in the loop that cascades through subsequent steps. Quality gates, independent review, and escalation paths are the architectural countermeasures.

This post is part of our Types of AI Agents series. For the foundational concepts, start with What Are AI Agents?. For how language models power agent reasoning, see LLM AI Agents. For a comparison of the platforms building on these architectures, see AI Agent Systems.