What Are AI Agents?

How autonomous AI systems work, why they differ from chatbots, and what makes them expensive to run

An AI agent is software that uses a large language model to pursue a goal. It decides what to do next, takes action, observes the result, and repeats until the goal is reached (or it hits a defined limit).

Unlike a chatbot that answers questions or a copilot that suggests completions, an agent operates with a degree of autonomy. It can call APIs, query databases, write and execute code, trigger workflows, and chain multiple steps together, all without human approval.

This changes what organizations can delegate to software. Tasks that previously required a person to coordinate, decide, and act can now be handed to a system that handles all three.

With traditional software, the quality of the output depends on how precisely you can specify every step. With agents, it depends on how clearly you can define the outcome you want.

In this article, we explain what AI agents are, how they reason and act, and what it takes to deploy them responsibly.

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AI Agents vs. Chatbots vs. Copilots: What's the Difference

The terms "agent," "chatbot," and "copilot" are often used interchangeably, but they describe fundamentally different systems with different cost profiles and governance challenges.

How Does a Chatbot Work?

A chatbot is a conversational interface powered by a large language model (LLM). It takes a user message, generates a response, and waits for the next message.

It has no memory between sessions (unless explicitly engineered), no ability to take external actions, and no autonomy. Customer service bots, FAQ assistants, and most virtual assistants used by banks are all chatbots at their core.

Take a sales research chatbot. A human sales rep submits a company name to a chatbot. The chatbot could answer questions about what's in the CRM, but it can't query it, synthesize the results, or take any action on its own.

How Does a Copilot Work?

A copilot augments a person's work in real time. An example is GitHub Copilot, which suggests code snippets, completes lines of code, and writes entire functions in real time. Microsoft Copilot drafts emails and summarizes documents.

Copilots are reactive: they respond to what the person is doing rather than pursuing goals independently. They may use tools (like code execution or web search), but a human remains in the loop at every step.

Using the same sales research example, a copilot might suggest the next line of an outreach email as the rep types it, but the rep still decides what to send and when.

How Does an AI Agent Work?

An agent receives a goal, such as "research these three competitors and draft a comparison report" or "process all incoming support tickets and route them to the right team," and executes autonomously.

It decides which tools to use, how many steps to take, when to retry, and when to stop. The person who triggered the agent may not see it again until it delivers a result or hits an exception.

Using the same sales research example, an agent might query a CRM for existing relationship history, search the web for recent news, pull financial data from a third-party API, and draft a briefing document with key talking points.

The rep receives a finished output minutes later. No instructions were given beyond the initial request. No steps were approved along the way.

Agent Workflows and Tool Use

What distinguishes an agent from a sophisticated prompt chain is tool use: the ability to interact with external systems, not just generate text. Common tool categories include:

Information retrieval. Web search, database queries, document retrieval (RAG), and API calls to external services.
Code execution. Writing and running code in sandboxed environments for data analysis, calculations, and deterministic logic.
System actions. Creating CRM records, sending emails, filing tickets, updating databases, and triggering CI/CD pipelines.
Communication. Messaging other agents, requesting human input, or \posting updates to tools like Slack or Teams.

Each tool call may involve external API charges, compute resources, or downstream system load (costs that sit outside the LLM invoice entirely).

Because the agent decides which tools to use and how often, a poorly designed agent encountering an ambiguous task can make 15 tool calls, retry several of them, and still not reach a satisfactory outcome.

That unpredictability is inherent, not incidental. It's the natural consequence of giving software the autonomy to decide its own execution path at runtime.

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Costs Associated With Running an AI Agent, Chatbot, and Copilot

The cost model for a chatbot is straightforward: one request in, one response out, billed by tokens consumed. Most customer service bots, FAQ assistants, and simple conversational interfaces are chatbots.

Costs for copilots are somewhat more variable than chatbots because copilots may run multiple model calls per interaction, but a person's attention span acts as a natural rate limiter.

For agents, autonomy is both the source of their value and the source of their risk. Agents can do in minutes what would take a human hours. But because the agent controls its own execution, it also controls its own cost.

A poorly constrained agent can enter loops, make redundant API calls, invoke expensive tools unnecessarily, or cascade through multi-step workflows that compound token costs at every stage.

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How Agents Make Decisions and Take Actions

At the core of every AI agent is a reasoning loop. The most widely adopted pattern is ReAct (Reason + Act), introduced in a 2022 paper by Yao et al. and presented at ICLR 2023. In a ReAct loop, the agent cycles through three phases:

Thought. The agent reasons about its current state, the goal, and what it should do next. The LLM generates this reasoning as natural language, literally a verbalized chain of thought.
Action. Based on its reasoning, the agent selects a tool and provides input. This might be a web search query, an API call, a database query, a code execution request, or a message to another agent.
Observation. The agent receives the action’s output and incorporates it into its context. It then loops back to the Thought phase to decide whether the goal has been achieved or whether another action is needed.

This loop repeats until the agent determines it has a satisfactory answer or until it hits a predefined limit on steps, tokens, or time.

ReAct is not the only pattern:

Plan-and-execute agents separate planning from execution: They generate a full plan upfront, then execute each step sequentially. This is more predictable but less adaptive. If one step fails, the agent may not recover gracefully without replanning.
Reflective agents add a self-critique layer, where the LLM evaluates its own output before proceeding, which improves quality but adds inference calls.
Human-in-the-loop agents pause at predefined checkpoints for human approval before taking high-stakes actions.

The choice of architecture affects both what the agent can do and what it will cost to run; flexibility and cost-predictability sit at opposite ends of the spectrum.

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How Agents Remember: The Four Types of Agent Memory

The ReAct loop explains how an agent reasons from one step to the next. It doesn't explain how an agent remembers anything beyond the current session, or why that limitation matters in production.

There are four memory types worth understanding:

In-context memory. Information held within the active prompt window. Fast and immediately accessible, but limited by the context window size and lost when the session ends.
External memory. A vector database or document store that the agent can query mid-task. This is the basis of retrieval-augmented generation (RAG). The agent doesn't "know" the information natively, but it can look it up. Slower than in-context, but effectively unlimited in scale.
Episodic memory. A record of past interactions that the agent can reference. This is what allows an agent to say, "Last time you asked me to do this, here's what happened." Most production agents don't have this yet, but it's an active area of development.
Semantic memory. This stores structured facts about the world, or about a specific user, organization, or domain, and updates them over time.

Most agents today operate with what's called in-context memory. This means that everything the agent knows is contained within the current prompt window. When the session ends, it's gone.

In-context memory works fine for self-contained tasks, but it breaks down quickly for anything that requires continuity, such as a support agent that should remember a customer's history, a research agent that should build on work it did last week, or a workflow agent that needs to track state across a multi-day process.

Knowing which memory types an agent has access to is important for two reasons. First, it determines what the agent is actually capable of. An agent with only in-context memory cannot reliably handle tasks that span sessions or require accumulated knowledge.

Second, it affects cost. External memory queries add latency and tool call costs. Episodic and semantic memory systems require infrastructure to maintain.

Memory is not free, and more capable memory is more expensive.

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Common Agent Architectures: Single-Agent, Multi-Agent, Orchestrated

There are three broad ways to deploy AI agents, each with different levels of complexity, flexibility, and cost. Knowing how these architectures differ matters because the architecture you choose significantly affects what your agents will spend.

Single-Agent

The simplest deployment is one agent assigned to one task with access to a defined set of tools. A customer support agent, for example, might have access to a knowledge base, a ticketing system, and an escalation API.

The scope of a single agent is bounded, its tool set is known, and its cost behavior is relatively predictable compared to more complex configurations.

Multi-Agent

Multi-agent systems deploy multiple specialized agents that collaborate on complex tasks. Rather than one agent with 20 tools, a multi-agent system might use a "researcher" agent that searches and summarizes, a "writer" agent that drafts content, and a "reviewer" agent that checks quality.

Each agent handles a narrower scope, allowing the system as a whole to tackle tasks that would overwhelm a single agent.

The tradeoff is cost. Each agent in the chain adds its own inference calls, tool invocations, and retry logic. A task that costs a dollar with a single agent can cost several dollars in a multi-agent system, simply because more agents are running in parallel or sequence.

Orchestrated

Orchestrated systems add a coordination layer on top of multi-agent setups. The coordination layer is a supervisor or control mechanism that routes tasks, enforces sequencing, and handles failures. This is where frameworks like CrewAI and LangGraph come in, and they take meaningfully different approaches.

CrewAI

CrewAI uses a role-based architecture modeled on how human teams operate. You define agents with explicit roles (researcher, analyst, writer), assign them goals, equip them with tools, and group them into a "crew."

Its architecture has two layers:

Crews: Teams of autonomous agents that plan and delegate among themselves
Flows: Deterministic, event-driven backbones that control the overall workflow

A Flow manages the state machine — routing tasks, enforcing sequencing, handling exceptions — while delegating specific steps to Crews that operate with autonomy within their scope.

DocuSign, for example, uses CrewAI Flows to orchestrate a sales pipeline acceleration system built on five agents: an Identifier, a Researcher, a Composer, a Validator, and an Orchestrator. The agents pull data from Salesforce and Snowflake to research leads, compose personalized outreach, and verify quality before sending.

LangGraph

Built by the LangChain team, LangGraph takes a graph-based approach. Instead of defining roles and letting agents negotiate, you define agents as nodes in a directed acyclic graph (DAG), with edges controlling how data flows between them.

A centralized StateGraph maintains context, stores intermediate results, and enables both parallel execution and conditional branching. LangGraph supports multiple orchestration patterns:

Supervisor agents that route tasks to specialists
Peer-to-peer handoffs, where agents transfer control directly
Hierarchical structures where sub-agents can themselves be graphs

The graph compiles before execution, validating connections and optimizing paths, which makes the workflow more predictable than a free-form agent conversation.

The Difference Between CrewAI and LangGraph

The core difference between the two is flexibility versus control. CrewAI's role-based autonomy is powerful for open-ended tasks, but that flexibility means more inference calls as agents deliberate, delegate, and self-critique. And more inference calls mean higher costs.

LangGraph's explicit graph structure reduces unnecessary inference but demands more upfront architectural work and can be harder to adapt when requirements change.

Both frameworks support assigning different models to different agents: a large, expensive model for complex reasoning and a small, cheap model for structured output.

This model-routing pattern can reduce costs, but it requires deliberate architecture. It doesn't happen by default.

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Why Agents Create Unique Cost and Governance Challenges

According to a 2025 survey from Benchmarkit and Mavvrik, 85% of organizations misestimate their AI costs by more than 10%. Agents are why. Unlike traditional software, agents don't have fixed resource envelopes. And without purpose-built economic controls, they won't fit neatly into any budget model you already have.

Every prior generation of enterprise software had predictable cost behavior. A SaaS seat costs a fixed monthly price. A cloud VM costs a known rate per hour. Even LLM API calls, while variable, follow a simple formula: tokens in × price per token = cost. You can estimate it before the call and verify it after.

Agents break this model. The cost of an agent task is determined at runtime by the agent's own decisions: how many steps it takes, which tools it invokes, how many tokens it consumes across potentially dozens of inference calls, and whether it succeeds on the first attempt or retries.

There is no fixed price per task, no predictable resource envelope, and often no real-time visibility into what the agent is spending as it runs. This produces several problems that traditional IT governance is not equipped to handle:

Cost is invisible in real time. Agents spend money autonomously, across dozens of inference calls and tool invocations, with no native mechanism to alert anyone as costs accumulate. By the time a human becomes aware of what a workflow spent, the spending is already done.
Costs compound across agent chains. In multi-agent systems, a single user request can trigger a cascade of agent interactions. Each agent in the chain adds its own inference, tool invocation, and retry costs. The total cost of the workflow is often several multiples of what any individual agent would cost in isolation.
There is no natural rate limiter. With chatbots, a human is in the loop; the speed of conversation limits the rate of API calls. With autonomously operating agents, the rate limiter is the LLM’s speed itself. An agent in a retry loop can burn through a budget in minutes.
Budget controls don't exist at the right level. Cloud budgets are set at the account or project level. AI budgets, when they exist, are typically set at the API key or department level. Neither maps to the unit that matters for agents: the individual task or workflow. If an agent has a budget of "as much as the API key allows," it effectively has no budget.

Taken together, these aren't edge cases or implementation failures. They're structural properties of how agents work. Managing them requires controls that traditional IT governance wasn't built to provide.

What This Means in Practice

Consider a customer support agent deployed to handle incoming tickets. Using a mid-range model, each support interaction might consume anywhere from 5,000 to 8,000 tokens across the reasoning loop. The agent reads the ticket, checks the knowledge base, drafts a response, and sometimes retries if its first attempt doesn't match the required format. At $0.01 to $0.03 per thousand tokens, each interaction costs between five and twenty-five cents.

Now scale that agent to 1,000 tickets per day. That's roughly 150 to 240 million tokens per month, running somewhere between $1,500 and $7,200 per month just in LLM inference. Still reasonable for a team that would otherwise require several full-time support staff.

The economics shift when that agent gets upgraded to a multi-agent workflow. A triage agent classifies tickets, a research agent pulls context from the CRM and knowledge base, and a response agent drafts the reply. Now each ticket triggers three agents instead of one, each carrying its own inference costs, tool calls, and retry logic. That 5,000 to 8,000-token interaction becomes 15,000 to 30,000 tokens, and the monthly LLM bill triples or quadruples.

For example, consider an e-commerce company that enables order-tracking workflows within an existing support agent. Token usage spikes by 300% with no change in ticket volume. The cost increase is driven entirely by the agent's expanded scope of action.

This is the environment in which Agent Debt accumulates. It isn’t the result of any single dramatic failure, but through the steady accretion of small, invisible costs across thousands of agent tasks per day.

Organizations that deploy agents without accumulating agent debt tend to do four things:

Treat architecture as a cost decision. The tools an agent can access, its loop iterations, its retry logic, and which model powers each step all directly affect spend. Assigning cheaper models to classification and routing tasks and reserving capable models for complex reasoning can reduce costs without meaningful quality loss.
Budget at the task level. Per-seat and per-API-key budgets don't map to how agents consume resources. Spend limits need to exist at the agent, task, and workflow level, and be enforced in real time.
Instrument for cost, not just performance. Any agent framework running in production should emit cost telemetry at the step level. For any agent running autonomously at scale, circuit breakers that halt runaway workflows before they breach a threshold are a requirement, not a nice-to-have.
Build controls before you need them. Every prior technology adoption cycle followed the same pattern: rapid adoption, cost surprises, and a scramble to build governance after the fact. Agent adoption is compressing that arc into months. The organizations that act now are better positioned than those that don't.

The companies that will manage agents well in production are the ones that treat them not as advanced chatbots but as autonomous economic actors. That framing changes how you architect, monitor, and govern them.

Cost isn't the only thing that can spiral when agents operate without guardrails. An agent that stays within budget can still send the wrong email, modify the wrong record, or execute the wrong transaction. That's why trust and oversight deserve as much deliberate attention as cost controls.

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Trust, Oversight, and Deciding How Much Autonomy to Give an Agent

When deploying an agent, most teams make an implicit decision about how much to trust it. That decision is often made by default rather than deliberately. The agent gets access to whatever tools are available, and it runs until it either succeeds or hits an error.

That's not a trust model. It's the absence of one. A more deliberate approach treats agent autonomy as a spectrum. At one end, fully supervised agents take no action without human approval at every step. At the other end, fully autonomous agents execute entire workflows without any human in the loop.

Most production deployments should sit somewhere in between, and where exactly depends on the stakes of the actions the agent can take.

A useful way to think about this is to categorize agent actions by their reversibility:

Reading data is low stakes. For instance, if the agent queries the wrong database, nothing breaks.
Writing data is higher stakes. A record created in a CRM can be deleted, but it takes effort.
Sending an email is essentially irreversible. You can't unsend a message to a customer.
Executing a financial transaction or modifying production infrastructure carries the highest risk. These actions are difficult or impossible to roll back and can have immediate real-world consequences.

A well-designed trust model maps action categories to approval requirements. Read operations can be fully autonomous. Write operations may warrant a review step before proceeding. Irreversible or high-stakes actions should require explicit human approval, regardless of how confident the agent appears.

This matters beyond risk management. Regulatory and legal frameworks are beginning to catch up with autonomous AI systems. Questions of liability (specifically, who is responsible when an agent takes a consequential action that causes harm) are increasingly relevant for organizations deploying agents at scale.

Building human oversight into agent workflows now is not just good engineering practice. It's a meaningful hedge against a compliance landscape that is still taking shape.

The companies that will deploy agents most successfully are not the ones that give agents the most autonomy. They're the ones that give agents exactly as much autonomy as the task warrants, and no more.

Deploying agents responsibly means managing two categories of risk in parallel: the economic risk of systems that spend money autonomously without controls, and the operational risk of systems that take consequential actions without appropriate oversight.

Agents are genuinely powerful, and the productivity gains are real. The organizations that will capture those gains most durably are the ones that pair autonomy with accountability, giving agents the access they need to do their jobs, and the guardrails that make that access sustainable.

If you're deploying agents and want visibility into what they're actually spending, Revenium helps engineering and finance teams track AI costs at the task and workflow level — in real time, not after the invoice arrives. Sign up to see how it works.