How AI Agents Actually Work: A Technical Deep Dive

Everyone talks about AI agents. Almost nobody explains how they actually work. If you've used Cursor, Claude Code, or any modern coding assistant, you've interacted with an agent — but the mechanics behind that interaction are surprisingly misunderstood, even by experienced engineers.

This is the technical deep dive. No hand-waving, no metaphors about "digital employees." We're going to break down the observe→think→act→observe loop, the ReAct framework, function calling, planning, memory, and multi-agent architectures — with enough detail that you could build your own agent after reading this.

At Webyot Technologies, we've built production agents for startups and use them daily in our AI-native development workflow. Everything here comes from building and shipping real systems, not from reading papers.

What Makes an Agent Different From a Chatbot

A chatbot takes input and produces output. That's it. You type a message, it generates a response, and the conversation is over (or continues with a new message). There's no intermediate reasoning, no tool use, no autonomous decision-making.

An AI agent operates in a fundamentally different paradigm. Instead of a single input→output turn, an agent runs in a loop:

• Observe — the agent receives input (a user message, an environment state, or a tool result).
• Think — the agent reasons about what it observed, decides what to do next.
• Act — the agent takes an action: calls a tool, generates code, sends a message, or queries a database.
• Observe — the agent receives the result of its action and loops back to "Think."

This loop continues until the agent decides it has completed the task — or hits a safety limit. The critical difference is that an agent has agency: it makes decisions about what to do next rather than simply predicting the next token in a response.

A chatbot is a function: response = f(message). An agent is a state machine: it maintains context, chooses actions, processes results, and iterates until the goal is achieved.

The ReAct Framework: Reasoning + Acting

The most influential framework for understanding agents is ReAct (Reasoning + Acting), published by Yao et al. in 2022. It's the foundation behind most production agent systems today, including the ones you use in your IDE.

ReAct defines a repeating cycle of three steps:

Step 1: Thought (Reasoning Trace)

The agent generates a thought — a natural language reasoning trace about what it should do next. This isn't the final answer; it's the agent "thinking out loud." For example:

Thought: "The user wants me to fix the login bug. I should first look at the authentication module to understand how login is handled, then check the error logs to see what's failing."

This reasoning trace serves two purposes: it helps the model plan its next action, and it creates a transparent audit trail that humans can inspect. When you see Claude Code or Cursor showing you its "thinking," that's the ReAct thought step.

Step 2: Action (Tool Selection)

Based on its thought, the agent selects an action — typically a tool call. The action includes which tool to use and what parameters to pass. For example:

Action: read_file(path="src/auth/login.ts")

The agent doesn't guess what's in the file. It actually calls a tool to read it. This is what separates agents from pure language models — agents interact with the real world through tools.

Step 3: Observation (Result Feedback)

After the action executes, the agent receives an observation — the result of the tool call. This could be file contents, an error message, API response data, test results, or any other output.

Observation: The contents of src/auth/login.ts, showing the authentication logic, token validation, and session management code.

The agent then loops back to Step 1 with this new information, reasoning about what to do next based on what it observed. This cycle repeats until the agent determines the task is complete.

Tool Use / Function Calling: How LLMs Invoke External Tools

Tool use is the mechanism that gives agents their power. Without tools, an LLM can only generate text. With tools, it can read files, execute code, query databases, call APIs, and interact with any system you connect it to.

Here's how function calling works under the hood:

Schema Definition

You define each tool as a schema — a JSON object that describes the tool's name, what it does, and what parameters it accepts. For example:

Tool schema: search_database — "Searches the product database by keyword. Parameters: query (string, required), limit (integer, optional, default 10)."

This schema is injected into the model's system prompt. The model sees all available tools and their descriptions before it starts reasoning.

Parameter Extraction

When the model decides to use a tool, it outputs a structured JSON object with the tool name and extracted parameters. The model's training enables it to extract parameters from natural language context. If the user says "find all orders from last week," the model extracts date_range: "last_week" from the conversational context.

Result Handling

The runtime executes the actual function (not the model — the model only generates the call), gets the result, and feeds it back to the model as an observation. The model then decides whether it needs more tool calls or can produce a final answer.

This is the key insight: the model never executes tools directly. It generates structured instructions for tool calls, and a separate runtime handles execution. This separation is what makes agents safe (with proper guardrails) — you can validate, rate-limit, or deny any tool call before it executes.

The Agent Loop in Detail

Let's walk through a complete agent loop step by step, using a real coding task as an example: "Fix the broken user registration flow."

Step 1 — Receive input: The user's request enters the agent's context. The agent has access to tool schemas, system instructions, and the conversation history.

Step 2 — Check if a tool is needed: The agent reasons: "I can't fix something I don't understand. I need to read the registration code first." It determines a tool call is required.

Step 3 — Select tool and generate call: The agent outputs: read_file(path="src/auth/register.ts")

Step 4 — Execute the tool: The runtime reads the file and returns its contents.

Step 5 — Observe the result: The agent receives the file contents and reads the registration logic.

Step 6 — Decide the next step: The agent reasons: "I see the issue — the email validation regex is wrong. But let me also check the test file to understand the expected behavior." It decides to make another tool call.

Step 7 — Repeat: The agent calls read_file(path="tests/auth/register.test.ts"), observes the test expectations, then calls edit_file to fix the regex, then calls run_tests to verify the fix.

Step 8 — Final answer: After observing that all tests pass, the agent decides no more actions are needed and generates a final response: "Fixed the email validation regex in register.ts. All tests pass."

This entire loop — from receiving the input to the final answer — might involve 5–15 tool calls, each preceded by a reasoning step. The agent autonomously decides which tools to use and when to stop.

Planning: The Plan-and-Execute Pattern

Simple ReAct agents react step-by-step, which works for straightforward tasks. But for complex tasks — "Build a user dashboard with authentication, charts, and data export" — you need planning.

The Plan-and-Execute pattern separates planning from execution:

Planner model: A (usually more powerful) model analyzes the task and generates a plan — a list of sub-tasks in order. For example: "1. Set up project structure. 2. Create auth middleware. 3. Build dashboard layout. 4. Add chart components. 5. Implement data export. 6. Write tests."

Executor model: A (usually faster, cheaper) model executes each sub-task one at a time, using the standard ReAct loop for each step.

Re-planning: After each sub-task completes, the plan can be updated. If step 3 fails because a dependency is missing, the planner can insert a new step to resolve it.

This pattern is powerful because it separates "what to do" from "how to do it." The planner thinks strategically; the executor thinks tactically. Most production coding agents use some variant of this pattern — Cursor's Composer mode, for example, generates a plan before making edits.

Reasoning Patterns: CoT, ToT, and Reflection

Agents use several reasoning patterns, often in combination:

Chain-of-Thought (CoT)

The simplest pattern. The model breaks down a problem into sequential steps and reasons through each one. "The user wants X. To get X, I need Y. To get Y, I need Z. Z requires..." Each step builds on the previous one in a linear chain.

CoT is effective for straightforward problems with a clear solution path. It's the default reasoning mode for most agents.

Tree-of-Thought (ToT)

Instead of a single chain, the model explores multiple branches of reasoning simultaneously. "I could solve this by approach A, approach B, or approach C. Let me evaluate each..." The model generates multiple possible solutions, scores them, and pursues the most promising branch.

ToT is more expensive (requires multiple inference calls) but dramatically better for problems with multiple valid approaches — architectural decisions, algorithm design, or debugging ambiguous issues.

Reflection (Self-Critique)

After generating a solution, the model critiques its own output. "I just wrote this code. Let me review it for bugs, edge cases, and performance issues..." The model acts as its own code reviewer, catching errors before a human sees them.

Reflection is what makes Claude Code and advanced Cursor agents so effective — they don't just write code, they review and iterate on it autonomously. This is also the most expensive pattern, as it requires additional inference passes.

Memory in Agents: Short-Term and Long-Term

Agents need memory to maintain context across interactions and learn from past experiences. There are two main types:

Short-Term Memory (Scratchpad)

The agent's conversation context — everything that's happened in the current session. This includes the user's messages, the agent's thoughts, all tool calls and their results, and any intermediate reasoning. It's stored in the model's context window and exists only for the duration of the session.

The scratchpad is what makes the ReAct loop work — the agent can reference earlier observations when reasoning about its next action. The limitation is the context window size: once you exceed it, older context gets truncated or summarized.

Long-Term Memory (Vector Store)

For agents that need to remember across sessions, long-term memory uses a vector database. Past interactions, learned preferences, and important facts are embedded as vectors and stored. When the agent needs to recall something, it performs a similarity search against the vector store.

Production coding agents use this to remember your project's conventions, your coding style, past bugs you've encountered, and decisions you've made. It's why Cursor gets better at understanding your codebase over time. We've written extensively about memory systems for AI agents if you want to go deeper.

Multi-Agent Architectures

When a single agent isn't enough, you use multiple specialized agents that collaborate:

Supervisor Pattern

One agent acts as the supervisor — it receives the task, decomposes it, and delegates sub-tasks to specialized worker agents. The supervisor monitors progress, handles failures, and synthesizes the final result. This is the most common multi-agent pattern.

Peer-to-Peer Pattern

Agents communicate directly with each other, passing messages and results. There's no central coordinator — agents negotiate and collaborate as peers. This is more flexible but harder to manage and debug.

Hierarchical Pattern

A tree structure where top-level agents delegate to mid-level agents, which delegate to bottom-level agents. Each level has different responsibilities and capabilities. This scales well for very complex systems but adds latency and cost.

Multi-agent architectures are powerful but come with trade-offs: higher cost (each agent call costs tokens), more complexity (debugging is harder), and coordination overhead (agents need to agree on shared state). Start with a single agent and only go multi-agent when you hit clear limitations.

The 8 Production Agent Patterns

After building and deploying agents in production, we've identified eight patterns that consistently appear in successful systems:

1. ReAct — The fundamental observe→think→act loop. Use this as your starting point for any agent.

2. Tool Use — Connecting the agent to external tools via function calling. The more capable your tools, the more capable your agent.

3. Planning — Plan-and-Execute for complex tasks. Essential for multi-step projects.

4. Reflection — Self-critique and iteration. Catches errors before humans see them.

5. Multi-Agent — Specialized agents collaborating. Use sparingly and only when a single agent isn't enough.

6. Human-in-the-Loop — Agents pause and ask for human approval before high-stakes actions. Critical for production safety.

7. Guardrails — Input validation, output filtering, tool call restrictions, and loop detection. Non-negotiable for production.

8. Evaluation — Automated metrics for task completion, quality, cost, and safety. You can't improve what you can't measure.

These patterns aren't mutually exclusive — production agents typically combine 3–5 of them. The skill is knowing which patterns to apply and when. We cover the practical implementation of these in our agent workflow guide.

Why Most Agent Projects Fail

Having built agents for multiple startups, we've seen the same failure patterns over and over:

No evaluation. Teams build agents and declare success based on a few demo runs. Without systematic evaluation — benchmark tasks, quality metrics, cost tracking — they can't detect when the agent regresses or compare different approaches objectively.

No guardrails. Agents make unexpected tool calls, hallucinate data, enter infinite loops, or execute destructive actions. Without guardrails — input validation, output filtering, tool call limits, and human approval for high-stakes actions — production agents are ticking time bombs.

Too much complexity. Teams build elaborate multi-agent systems with custom orchestration, memory layers, and reflection loops when a single well-prompted agent with good tools would solve 90% of the problem. Complexity kills velocity and makes debugging nearly impossible.

The most successful agent projects we've seen follow a simple recipe: start with the simplest possible agent (single ReAct loop), add tools incrementally, measure everything, and only add complexity when the data justifies it. This is the same approach we take with MCP server integrations — start simple, iterate based on real usage.

For a broader view of how these agent patterns apply to coding specifically, see our top 10 coding agents comparison.

Agent Architecture at a Glance

How Webyot Technologies Builds Production Agents

At Webyot Technologies, we apply these patterns every day. Here's our approach to building agents for startup products:

Start with ReAct + Tools. Every agent begins as a simple ReAct loop with well-defined tools. We invest heavily in tool quality — clear schemas, good error messages, and fast execution. A mediocre model with great tools outperforms a great model with mediocre tools.

Add planning only when needed. If the task requires more than 5–7 tool calls, we add a planning layer. Otherwise, the ReAct loop handles it. Most tasks don't need planning.

Evaluate from day one. We create a benchmark suite of real tasks before building the agent. Every change is measured against this suite. If we can't measure it, we don't ship it.

Guardrails are not optional. Every tool has validation. Every agent has a max iteration limit. High-stakes actions (database writes, file deletions, API calls with side effects) require human approval. This isn't paranoia — it's engineering discipline.

This approach lets us build and deploy production agents in weeks, not months. The patterns are well-established; the hard part is applying them correctly to your specific domain.

If you're building an AI-powered product and want to leverage these patterns without the trial and error, talk to our team. We've already solved the hard problems so you don't have to.

What's Next: The Agent Landscape in 2026

Agent architectures are evolving rapidly. Here's what we see coming:

• Standardized tool protocols like MCP (Model Context Protocol) are making tool integration universal — no more custom integrations for every API.
• Agentic RAG — agents that don't just retrieve documents but reason about what to retrieve, when to retrieve it, and how to synthesize results across multiple sources.
• Autonomous coding agents that work for hours on complex features, run their own tests, and submit PRs for human review. The tools listed in our top coding agents guide are already moving in this direction.
• Agent-native frameworks — codebases designed from the ground up to be understood and modified by AI agents, with structured documentation, explicit conventions, and machine-readable architecture.

The engineers who understand these internals — not just how to use agents, but how they work under the hood — will have a massive advantage in building the next generation of AI products.