AI Agents — How They Work and When to Build One

What an AI Agent Is

An AI agent is a system where a language model controls a multi-step process: it decides what to do next, takes action (calls a tool, queries a database, writes to an API), observes the result, and repeats until the task is complete. The critical difference from a chatbot is autonomy over a sequence of steps. A chatbot responds to one message with one response. An agent pursues a goal across multiple steps, choosing its own path through the available tools.

The four components that distinguish an agent from a simple LLM call are:

LLM backbone: the reasoning engine — GPT-4o, Claude 3.5 Sonnet, Llama 3, or similar. It interprets the goal, chooses actions, and synthesizes results.
Tools: functions the LLM can call — web search, database queries, code execution, API calls, file reads, browser control. Without tools, the LLM can only generate text; with tools, it can take real-world actions.
Memory: what the agent retains across steps. Short-term memory is the conversation context within a single session. Long-term memory uses a vector database or key-value store to persist information across sessions and retrieve relevant context when needed.
Planning: how the agent breaks down a complex goal into subgoals and decides the order of actions. Some agents plan explicitly (writing out a plan before executing), others plan implicitly (choosing the next action at each step based on context so far).

The Core Loop: Observe, Reason, Act

The ReAct pattern (Reasoning + Acting) describes the basic agent loop:

Observe: receive the goal and the current state (prior observations, tool outputs, memory).
Reason: think through what to do next. In ReAct implementations, this is an explicit “Thought:” step where the model narrates its reasoning before choosing an action.
Act: call a tool or produce a final answer.
Observe the result: read the tool output and return to step 2.

This loop continues until the agent decides the task is complete (or reaches a maximum step limit).

Example: a research agent tasked with “find the three largest fintech funding rounds in Q1 2025 and summarize what each company does.” The loop might run: search for Q1 2025 fintech rounds → parse results → identify top three → search each company name → synthesize summaries → return answer. Each step’s output feeds the next step’s reasoning. A simple LLM call with no tools could not do this reliably — it would hallucinate numbers from training data.

Components in Detail

LLM Backbone

Model choice affects every aspect of agent behavior: reasoning quality, tool-use reliability, context length, cost, and latency. Stronger models (GPT-4o, Claude 3.5 Sonnet) reason better and make fewer errors in tool selection and argument formation — important for long multi-step tasks. Smaller models (Llama 3 8B, Mistral 7B) are faster and cheaper but error more frequently in complex chains. For agents that run many steps or handle edge cases, use the most capable model you can afford for that task’s error sensitivity.

Tools

Tools are functions defined with a schema (name, description, parameters, return type) that the LLM can call by outputting a structured JSON action. The quality of tool descriptions matters as much as the tools themselves: an LLM decides which tool to call based on its description. Vague descriptions cause incorrect tool selection. Tool design principles: one tool per distinct capability, clear parameter names, explicit descriptions of what the tool returns, and error handling that returns useful information rather than generic failure messages.

Memory

Short-term memory is the message history in the context window. It has a hard limit (128K tokens for most current models) and grows with each agent step, increasing cost and eventually hitting the limit on long tasks. Strategies to manage this: summarization (compress earlier parts of the history), sliding windows (drop old messages), and selective memory (only keep the last N steps plus the initial goal).

Long-term memory stores information that should persist across sessions — user preferences, past task outcomes, domain facts. Vector databases (Pinecone, pgvector, Qdrant) store embeddings of past interactions; the agent retrieves relevant memories at the start of each session based on semantic similarity to the current task.

Planning

Explicit planning agents write a plan before executing: “Step 1: search for X. Step 2: filter results by Y. Step 3: write summary.” The LLM follows the plan, updating it if steps fail or new information changes the approach. This improves reliability on complex tasks but adds latency and tokens.

Implicit planning — choosing the next action at each step without a written plan — is simpler and works well for shorter tasks. Most ReAct-style agents use implicit planning.

Frameworks: Honest Comparisons

LangChain agents were the first widely-used agent framework and have broad tool integration and community support. They abstract away the agent loop and tool calling mechanics, making it fast to prototype. The tradeoff: heavy abstraction makes debugging harder, and the framework has historically had reliability issues on complex multi-step tasks. Better for prototyping than production systems with complex control flow.

LangGraph (from the LangChain team) models agent behavior as a directed graph of nodes (LLM calls, tool calls, conditional logic) and edges (transitions between nodes). This gives explicit control over agent control flow — you can define exactly what happens when a tool fails, when to loop back, when to branch to a different sub-agent. Better for production agents with complex requirements. Steeper learning curve than LangChain agents.

CrewAI focuses on multi-agent systems — multiple specialized agents (a researcher, a writer, a reviewer) collaborating on a task with defined roles and hand-off protocols. It’s opinionated and easy to get started with for multi-agent workflows. Less flexible than LangGraph for custom control flow.

Custom implementations using OpenAI or Anthropic tool-use APIs directly are more work upfront but give complete control. For production systems where reliability and debuggability are critical, a thin custom implementation is often more maintainable than a framework that abstracts away what you need to see.

Real Use Cases

Back-office automation. An agent that processes incoming vendor invoices: extracts line items (tool: document parser), checks against purchase orders (tool: ERP API query), flags discrepancies (tool: send Slack message), and posts approved invoices to the accounting system (tool: accounting API write). Steps a human would take in 15 minutes per invoice; an agent handles in under a minute with exception routing for edge cases.

Research agents. An agent that monitors a topic area: runs daily web searches, reads relevant documents, extracts key facts, maintains a persistent knowledge store, and produces a weekly digest. The value is that it tracks state across sessions — it knows what it already reported and focuses on new developments.

Customer support agents. Tier-1 support agents that can look up account information, check order status, process simple refunds, and escalate complex cases to human agents with full context. These require careful scope definition — the agent should have exactly the tools needed for its tier, no more.

Coding agents. Agents that can read a codebase, write code, run tests, observe test output, fix failures, and iterate. GitHub Copilot Workspace and Cursor’s agent mode are commercial implementations. For custom workflows — automated PR review, refactoring pipelines, code generation from specs — custom coding agents are an active engineering area.

When Agents Are Right and When They’re Not

Agents are appropriate when:

The task genuinely requires multiple sequential steps where each step’s output informs the next.
The steps are too varied or dynamic to model as a fixed pipeline.
You need to handle unexpected situations by reasoning, not by branching logic.
The task is repetitive enough to justify the engineering investment.

Agents are not the right architecture when:

A deterministic pipeline would do the job. If the steps are always the same and the outputs are predictable, build a pipeline. Agents add unpredictability without benefit in this case.
Reliability requirements are very high and error tolerance is very low. Agents can and do fail mid-task. For critical workflows (financial transactions, medical records), deterministic code with LLM calls at specific steps is safer.
The task is simple enough to handle in a single LLM call. Not everything needs an agent.
You don’t have the infrastructure to evaluate and monitor multi-step behavior.

Production Challenges

Reliability. Agents fail in ways that pipelines don’t: the LLM can select the wrong tool, form invalid arguments, misinterpret a tool’s output, or get stuck in loops. Production agents need retry logic, step-level error handling, maximum iteration limits, and fallback paths. A single-point-of-failure agent that errors out silently is not production-ready.

Cost at scale. An agent that runs 10 LLM calls per task at $0.01 per call costs $0.10 per task. At 10,000 tasks per day, that’s $1,000/day. Model choice and step count both drive cost significantly. Profile your agent’s token usage before scaling.

Evaluating multi-step behavior. Standard evals (input → expected output) don’t capture agent failures well. An agent can produce the right final answer via the wrong path, or fail at step 7 of 10 and partially succeed. Agent evals need to check intermediate steps, tool selection accuracy, and final outcomes separately. This is harder to build than single-call evals and is the area where production agent development is currently most challenging.

Latency. Multi-step LLM chains are slow. A 10-step agent with 2 seconds per step takes 20 seconds. This is acceptable for background tasks; it’s not acceptable for interactive user-facing applications. Design around latency: stream final responses where possible, run parallel sub-tasks when steps are independent, and set user expectations if the agent runs asynchronously.