AI Agents Guide — How LLM Agents Work in Production (2026)

1. Introduction and Motivation

Why Agents Represent a Fundamental Shift

For the first three years of mainstream LLM adoption (2022–2024), the dominant architectural pattern was RAG: retrieve relevant context, inject it into a prompt, generate a response. This pattern is powerful, predictable, and well-understood. It solves a specific class of problem well — answering questions over a fixed corpus of documents.

But RAG is fundamentally passive. It cannot take actions. It cannot call an API, execute code, search the web, update a database, or coordinate with another system. You get one round of retrieval, one call to the LLM, one response. If the task requires more than that — if the answer depends on something that must be computed, fetched, or decided dynamically — RAG cannot do it.

Agents exist to solve this. An agent is an LLM-powered system that can reason about what actions to take, execute those actions using tools, observe the results, and continue reasoning until a task is complete. This turns the LLM from a question-answering machine into an autonomous task executor.

This is not a minor extension of RAG. It is a different architectural model with different design patterns, different failure modes, different testing strategies, and different operational requirements.

What You Will Learn

This guide covers:

• How agents reason using the ReAct loop
• How tool use works at a technical level
• How memory systems enable agents to reason across time and context
• The three primary agentic workflow patterns (sequential, parallel, supervisor)
• How multi-agent systems divide and coordinate complex tasks
• Where agents fail in production and how to design against those failures
• What interviewers expect when discussing agent architecture

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2. Real-World Problem Context

The Limits of Stateless LLM Calls

Consider a realistic business problem: “Given a company name, find their latest 10-K filing, extract the revenue figures, compare them to last year, and send a summary to Slack.”

A RAG system cannot do this. The information is not in a pre-indexed corpus. The task requires multiple steps: search the SEC EDGAR database, fetch a specific document, parse structured financial data, perform arithmetic, format a message, and call the Slack API. Each step depends on the output of the previous one.

This is the class of problem agents are designed for: multi-step tasks that require dynamic action and depend on intermediate results.

The Shift in Production Requirements

From 2024 onward, the most impactful GenAI applications in production share a common pattern: they do not just answer questions — they complete tasks. Examples from production systems at scale:

• Customer support agents that look up account history, check inventory, create tickets, and send confirmation emails without human intervention
• Code review agents that clone a repository, run tests, analyze failures, propose fixes, and open pull requests
• Research agents that search the web, synthesize findings across multiple sources, and produce structured reports
• Data pipeline agents that monitor for anomalies, diagnose root causes, and initiate remediation workflows

These are not demos. These are production systems running at scale. They require a fundamentally different architecture than a document Q&A system.

Why Agents Are Harder to Build Than They Look

Agents introduce non-determinism at every level. A RAG system’s behavior is largely predictable: given the same query, retrieval will return the same documents, and the LLM response will be similar. An agent’s behavior is significantly less predictable: the LLM decides what actions to take, and those actions affect what happens next. A different tool result leads to a different next thought, which leads to a different next action.

This means:

• Testing requires evaluation of multi-step trajectories, not just final outputs
• Debugging requires trace-level visibility into every thought and action
• Cost is harder to bound because the number of LLM calls depends on task complexity
• Latency is harder to guarantee for the same reason
• Failures can cascade — one bad tool call leads the agent down an unrecoverable path

Building production agents requires understanding all of these failure modes before you start.

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3. Core Concepts and Mental Model

The ReAct Loop

The dominant reasoning pattern for LLM agents is ReAct (Reasoning + Acting), introduced in a 2022 Google Research paper and now the foundation of virtually every production agent framework.

The core insight of ReAct is this: before taking any action, the agent generates an explicit thought. This thought serves as a chain-of-reason that improves action quality and makes the agent’s behavior interpretable.

The loop proceeds as follows:

1. Observe: Read the current context — the original request, prior thoughts, prior tool results
2. Reason: Generate a thought that explains the current situation and what to do next
3. Act: Select and call a tool, or decide the task is complete and generate a final answer
4. Observe: Read the tool’s result and add it to context
5. Repeat until the task is complete or a termination condition is reached

This is not a metaphor. In LangGraph and most agent frameworks, this loop is literally implemented as a cycle in a graph. The same “reasoning node” is called multiple times until the agent produces a final answer.

📊 Visual Explanation

The ReAct Loop — How an Agent Processes a Request

Each iteration: observe context, reason about the next action, execute it, then observe results. Repeats until the agent decides the task is complete.

Pause

ObserveRead context window

User Request

Prior Thoughts

Tool Results

ReasonLLM generates thought

What do I know

What do I need

Which action

ActExecute tool or finish

Select Tool

Form Parameters

Call API or Return

UpdateIntegrate observation

Parse Result

Append to Context

Done or loop back

Idle

What Is a Tool?

A tool is any callable function that the agent can use to interact with the world outside its context window. Tools are the mechanism by which agents take action. Without tools, an agent is simply a very elaborate chatbot.

Tools are defined by three things:

1. A name that the LLM uses to refer to it
2. A description that tells the LLM when and why to use it (this is critically important — poor descriptions are one of the most common causes of agent failures)
3. A parameter schema that defines what inputs the tool accepts (usually a JSON schema)

When the LLM decides to use a tool, it generates a structured JSON object containing the tool name and parameter values. The agent framework parses this output, routes the call to the correct function, executes it, and returns the result as text back into the context window.

Modern LLMs do not actually “call” anything — they generate text. The framework interprets that text as a function call and performs the execution. This means tool calling is fundamentally a prompt engineering problem: the LLM must be able to read a tool description and reliably generate valid JSON for it.

What Is Agent Memory?

An agent without memory can only reason over what is in its current context window. For short tasks, this is sufficient. For long-running tasks, tasks that span multiple sessions, or tasks that require recall of prior interactions, you need a memory architecture.

Memory in agents is not a single concept. There are four distinct layers, each serving a different purpose:

Memory Type	Scope	Storage	Purpose
Working Memory	Current session	Context window	Active reasoning state, recent messages, tool outputs
Episodic Memory	Recent history	External store (database)	Session summaries, key facts from prior interactions
Semantic Memory	Long-term knowledge	Vector store	User preferences, domain knowledge, persistent facts
Procedural Memory	System-level	System prompt	Tools available, behavioral instructions, role definition

📊 Visual Explanation

Agent Memory Architecture

Four memory layers that enable agents to reason across time, sessions, and domain knowledge

Pause

Working Memory

Context window — active conversation, tool outputs, current task state (8K–200K tokens)

Episodic Memory

Session history — summaries and key facts retrieved by session ID or recency

Semantic Memory

Long-term knowledge — vector-indexed documents, user preferences, persistent domain facts

Procedural Memory

Skills and schemas — tool definitions, system instructions, behavioral constraints in system prompt

Idle

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4. Step-by-Step Explanation

How a Single Agent Processes a Request

Walk through what actually happens when you send a message to an agent:

Step 1: System prompt assembly

Before the LLM sees your message, the framework constructs a system prompt containing:

• The agent’s role and behavioral instructions
• Tool definitions (names, descriptions, parameter schemas)
• Any persistent context from prior sessions (episodic/semantic memory retrieval)
• Any constraints or guardrails

This system prompt can be very long — 2,000–10,000 tokens is common for a well-equipped agent.

Step 2: First LLM call — reasoning

The LLM receives the system prompt plus your user message. It generates a response that may be either:

• A tool call (structured JSON requesting a specific tool with parameters), or
• A final answer (the task is complete and the agent responds directly)

If the LLM generates a tool call, execution continues. If it generates a final answer, the loop terminates.

Step 3: Tool execution

The framework extracts the tool call from the LLM’s output, validates the parameters against the schema, and executes the function. The result (success or failure, with output) is formatted as text.

Step 4: Context update

The tool result is appended to the conversation as a “tool” message. The full context now contains: system prompt + user message + prior thoughts + tool result.

Step 5: Second LLM call — continued reasoning

The LLM reads the updated context and reasons again. It may call another tool, or it may now have enough information to produce a final answer.

Step 6: Termination

The loop continues until one of three conditions is met:

• The LLM produces a final answer
• A maximum iteration limit is reached (fail-safe against infinite loops)
• An explicit termination condition is triggered (error, timeout, human interrupt)

The Role of Structured Output

Tool calling relies on the LLM generating valid, parseable output. Modern LLMs (GPT-4, Claude 3.5+, Gemini 1.5+) support function calling mode — a native capability where the model is specifically fine-tuned to produce valid JSON for function calls, separate from its free-form text generation.

When using function calling mode, the LLM cannot “accidentally” mix its reasoning text with the tool call JSON. The tool call is produced in a separate, structured channel. This dramatically improves reliability compared to parsing tool calls from raw text.

Always prefer native function calling over ReAct prompt engineering with text parsing. The reliability difference in production is significant.

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5. Architecture and System View

Single-Agent vs Multi-Agent Systems

A single agent handles an entire task by itself, cycling through the ReAct loop until completion. This is appropriate for tasks that:

• Have a clear, single goal
• Require a bounded number of steps (typically fewer than 10–15 tool calls)
• Do not require specialized sub-domains of expertise

Multi-agent systems divide a complex task across multiple specialized agents. Each agent is an expert in a narrow domain: one agent does web research, another writes code, a third handles data analysis. A coordinating layer routes work between them.

Multi-agent systems are appropriate for tasks that:

• Require genuine expertise across multiple domains
• Benefit from parallel execution of independent subtasks
• Are too long for a single context window
• Require specialized reasoning at each step

The decision between single and multi-agent is not about capability — a single agent with enough tools can theoretically handle most tasks. It is about reliability, cost, and maintainability. A specialist agent with 5 tools and a tight system prompt is far more reliable than a generalist agent with 30 tools and an overloaded system prompt.

The Three Agentic Workflow Patterns

📊 Visual Explanation

Agentic Workflow Coordination Patterns

Three fundamental patterns for orchestrating multi-step AI tasks. The right pattern depends on task structure and dependency graph.

Pause

Sequential

Steps execute in order

Input

Agent: Research

Agent: Analyze

Agent: Write

Final Output

Parallel

Independent steps concurrent

Input

Branch: Web Search

Branch: DB Lookup

Branch: Cache Check

Merge + Output

Supervisor

Orchestrator delegates

Input

Orchestrator Agent

Specialist: Research

Specialist: Code

Orchestrator: Output

Idle

Sequential pattern

Steps execute in order. The output of each agent becomes the input to the next. Use this when each step produces context that subsequent steps depend on, and there is no opportunity for parallelism. Research → Analysis → Writing is a natural sequential pipeline.

Parallel pattern

Independent subtasks execute concurrently and their results are aggregated by a coordinator. Use this when subtasks do not depend on each other. Searching three different data sources simultaneously, then merging results, is the canonical example. Parallel patterns are underused — many sequential pipelines contain genuinely independent steps that could be parallelized for significant latency improvement.

Supervisor pattern

A top-level orchestrator agent breaks down the task, delegates subtasks to specialist agents, collects results, and decides the next step or final output. This is the most flexible and most complex pattern. LangGraph’s supervisor pattern and AutoGen’s group chat both implement variations of this. Use it for complex, open-ended tasks where the subtask breakdown is not fully known in advance.

Human-in-the-Loop Architecture

Production agents rarely operate completely autonomously. Most production deployments use human-in-the-loop checkpoints at specific points:

• Pre-execution approval: High-risk actions (deleting data, sending emails, making purchases) require human confirmation before proceeding
• Mid-task interruption: Long-running tasks surface progress at defined checkpoints and allow humans to redirect
• Post-task review: The agent completes the task but waits for human review before finalizing output

LangGraph implements human-in-the-loop through breakpoints — explicit pause points in the execution graph where the workflow suspends and waits for external input. The state is persisted to a checkpointer (database), so the workflow can be resumed after the human approves.

This pattern is non-negotiable for production agents that take actions with real-world consequences.

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6. Practical Examples

Example 1: A Research Agent

Task: “Find the three most cited papers on attention mechanisms published in 2023 and summarize their key contributions.”

Tool set:

• search_arxiv(query: str, max_results: int) — searches arXiv by keyword
• fetch_paper_details(arxiv_id: str) — retrieves title, abstract, citation count
• rank_by_citations(papers: list) — sorts papers by citation count

Execution trace:

1. Thought: “I need to search arXiv for attention mechanism papers from 2023”
2. Call: search_arxiv(query="attention mechanisms 2023", max_results=20)
3. Observe: Returns 20 results with IDs
4. Thought: “I need citation counts to rank them. Let me fetch details for all 20”
5. Call: fetch_paper_details × 20 (in parallel if framework supports it)
6. Call: rank_by_citations(papers=[...all 20...])
7. Thought: “I have the top 3. Now I’ll summarize using the abstracts I already have”
8. Final answer: Formatted summary of the three papers

This trace shows why agents need good tools. If fetch_paper_details has a poor description or unreliable output formatting, step 4 fails. If the ranking tool does not handle ties consistently, step 6 produces unreliable output. Each tool is a potential failure point.

Example 2: A Code Review Agent

Task: “Review this pull request and add inline comments for any bugs, security issues, or performance problems.”

Tool set:

• get_pr_diff(pr_url: str) — fetches the diff
• run_static_analysis(code: str, language: str) — runs linting and security scan
• post_pr_comment(pr_url: str, file: str, line: int, comment: str) — posts inline comment

Key design decision: The final action (post_pr_comment) has real-world consequences. This is where you add a human-in-the-loop checkpoint. The agent prepares all comments as a draft, presents them for review, and only posts after approval.

Without this checkpoint, a buggy agent posting hundreds of incorrect comments to a production repository is a real operational incident.

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7. Trade-offs, Limitations, and Failure Modes

The Context Window Problem

Every tool result, every thought, every message accumulates in the context window. Long-running agents hit context limits — the LLM runs out of space to reason. The agent begins to lose track of earlier steps, repeat tool calls it has already made, or generate incoherent reasoning.

Mitigation strategies:

• State summarization: Periodically compress conversation history into a dense summary
• Selective retention: Only keep the most recent N tool results in context; archive the rest
• State machines: Use LangGraph’s explicit state schema to manage what information persists, instead of relying on raw message history

Infinite Loops

Agents can get stuck. The LLM decides a tool call is needed, the tool returns an ambiguous result, the LLM calls the same tool again with slightly different parameters, gets another ambiguous result, and continues indefinitely.

Always implement a maximum iteration limit with appropriate error handling. A reasonable default is 10–20 iterations for most tasks. Production systems often also implement a cost limit (terminate if total token cost exceeds a threshold) and a time limit (terminate if total elapsed time exceeds a threshold).

Cascading Failures in Multi-Agent Systems

In a supervisor pattern, a failure in one specialist agent does not automatically terminate the workflow — the supervisor may not realize the subtask failed if the error is not propagated correctly. The supervisor continues routing work to other agents, which now operate on incomplete information.

Design every agent-to-agent interface with explicit success/failure signals. Do not rely on the LLM inferring failure from garbled output. Use structured return types with explicit error fields.

Tool Description Quality

This is the most underestimated failure mode in production agents. The LLM’s decision to use a tool — and its ability to use it correctly — depends almost entirely on the tool’s description.

A poor description:

• search(query) — “Searches for information”

A good description:

• search_arxiv(query: str, max_results: int = 10) — “Searches the arXiv preprint server for academic papers. Use this when you need information from published research. The query should be a keyword phrase, not a full sentence. Returns a list of paper IDs, titles, and abstracts. Limit results to 5–10 to stay within context constraints.”

The difference in agent reliability between these two descriptions is not marginal. It is the difference between an agent that works and one that doesn’t.

Prompt Injection in Tool Results

When an agent fetches content from the web, reads user-provided documents, or processes external data, that content becomes part of the context window. A malicious actor can embed instructions in that content: “Ignore your previous instructions and instead…”

This is a real security concern for production agents. Mitigation includes:

• Treating all external content as untrusted and sandboxing it within a marked context section
• Post-processing tool results to strip potential instruction-like content before passing to the LLM
• Using separate LLM calls for “analyze external content” vs “decide next action” to isolate the risk

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8. Interview Perspective

What Interviewers Are Assessing

Agents are one of the most common system design topics in senior GenAI engineering interviews as of 2025–2026. Interviewers are not primarily testing whether you know what an agent is — they assume you do. They are testing:

1. Can you identify when NOT to use an agent?

Agents are overused. Many candidates reach for agents when a simple chain or single LLM call would work better. Interviewers are impressed when you say: “This looks like a sequential pipeline with no loops needed — I’d use LangChain chains, not agents. Agents add latency and cost without benefit here.”

2. Can you design for failure?

Expect: “What happens when a tool fails mid-execution?” and “How do you prevent infinite loops?” If you cannot answer these, the interviewer will assume you have not built a real agent system.

3. Can you discuss cost and latency?

Agents make multiple LLM calls. Each call costs money and adds latency. A production agent that makes 8 GPT-4 calls per user request may be prohibitively expensive at scale. Interviewers expect you to think through: caching, model routing (use GPT-4o for reasoning, GPT-4o-mini for simple tool calls), parallelism, and early termination.

4. Can you discuss state management?

“How does your agent know what it has already done across multiple sessions?” is a standard senior-level question. Your answer should cover LangGraph checkpointers, episodic memory patterns, and the difference between session state and long-term memory.

Common Interview Questions on Agents

• Walk me through how a ReAct agent processes a user request step by step
• How do you prevent an agent from running indefinitely?
• You have an agent that needs to take a high-stakes action (delete a record). How do you design human-in-the-loop approval?
• When would you choose a multi-agent system over a single agent?
• How do you test an agent’s behavior systematically?
• How do you handle tool failures gracefully?
• What are the security implications of giving an agent web search access?
• Design an agent that can autonomously handle customer support tickets end-to-end

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9. Production Perspective

How Real Companies Deploy Agents

The gap between a demo agent and a production agent is large. Demo agents:

• Run against a handful of hand-picked test cases
• Have no cost or latency constraints
• Operate in a controlled environment with predictable tool outputs
• Do not need to handle concurrent users, adversarial inputs, or partial failures

Production agents at companies like Salesforce, Intercom, and GitHub Copilot:

• Handle thousands of concurrent sessions
• Operate under strict latency SLAs (<30 seconds for most tasks, <5 seconds for interactive ones)
• Have hard cost budgets per task
• Require complete audit trails for every action
• Run in sandboxed environments with strict permission scoping

Cost management in production

The most common cost reduction strategies:

• Model routing: Use a smaller, cheaper model for simple reasoning steps (GPT-4o-mini, Claude Haiku) and a larger model only when necessary
• Aggressive caching: Cache tool results for a short TTL to avoid redundant API calls across similar requests
• Iteration limits: Hard caps prevent runaway costs from looping agents
• Prompt compression: Summarize older context instead of keeping full message history

Observability for agents

Standard application monitoring is insufficient for agents. You need trace-level observability that captures every thought, every tool call, every tool result, and every token count for every iteration of every agent invocation. Tools like LangSmith, Arize, and Helicone provide this.

Without this, debugging a production agent failure is nearly impossible. You cannot reproduce agent behavior from logs alone — you need the full execution trace.

Sandboxing for code-executing agents

Agents that can execute code (Python interpreters, shell access) require strict sandboxing. Code execution must happen in isolated containers with no network access, no filesystem access outside a designated scratch directory, and resource limits (CPU, memory, execution time). A code-executing agent running in production without sandboxing is a remote code execution vulnerability.

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10. Summary and Key Takeaways

The Mental Model

An agent is a loop: observe → reason → act → observe. This loop continues until the task is complete or a termination condition fires. The LLM provides the reasoning; tools provide the ability to act; memory provides persistence across the loop and across sessions.

When to Use Agents

Scenario	Use Agent?	Reason
Multi-step task with dynamic tool use	Yes	Agents designed for this
Single question over fixed documents	No	RAG is simpler and more predictable
Task with clear sequential steps and no loops	Probably not	A simple chain suffices
Task requiring real-world actions	Yes	Agents with tools handle this
Long-running task spanning multiple sessions	Yes	With memory architecture
High-stakes actions requiring approval	Yes	With human-in-the-loop

Core Engineering Principles for Agents

1. Write precise tool descriptions — this is the highest-leverage quality improvement you can make
2. Always set iteration limits — never let an agent run unbounded
3. Use human-in-the-loop for irreversible actions — treat this as non-negotiable
4. Build trace-level observability before going to production — you cannot debug what you cannot see
5. Start with a single agent — only move to multi-agent when you have a clear reason
6. Design for failure at every tool boundary — every tool call can fail; your agent needs to handle it gracefully
7. Sandbox code execution — no exceptions for production systems

Official Documentation and Further Reading

Frameworks:

• LangGraph Documentation — Official guide to building stateful agent workflows
• LangGraph Guides — Hands-on walkthroughs for common agentic patterns
• LangSmith — Observability and tracing for LangChain/LangGraph agents

Research:

• ReAct: Synergizing Reasoning and Acting in Language Models — The original ReAct paper (Yao et al., 2022)
• ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs — Deep dive on tool use in LLMs

Evaluation and Production:

• LangSmith Evaluation Docs — How to evaluate agent trajectories systematically
• Arize AI — ML observability platform used in production LLM deployments
• Helicone — LLM observability with cost and latency tracking

Related

• Agentic Frameworks Comparison — LangGraph vs CrewAI vs AutoGen: which to use and when
• LangChain vs LangGraph — Understanding the architectural difference between pipelines and graphs
• Cloud AI Platforms — Deploying agents on AWS Bedrock Agents, Google Agent Builder, and Azure Copilot Studio
• AI Coding Environments — How Cursor, Claude Code, and GitHub Copilot use agentic workflows in your development environment
• Essential GenAI Tools — The full production tool stack for GenAI engineers
• GenAI Interview Questions — Practice questions on agents and architecture

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Last updated: February 2026. Agent patterns and framework capabilities evolve rapidly; verify specific framework APIs against current documentation.