How Do AI Agents Work? The Agent Loop Explained

A chatbot waits for the next prompt. An AI agent keeps working. It can interpret a goal, retrieve live context, choose a tool, take action, inspect the result, and adjust its approach without receiving instructions at every step.

So, how do AI agents work in 2026? They run through an iterative decision loop powered by a language model, business data, memory, tools, and guardrails. An AI Sales Assistant, for example, can search a CRM for stalled deals, review the interaction history, identify the likely blocker, prepare a recovery plan, and update approved fields. The model provides the reasoning, but the agent architecture turns that reasoning into controlled execution.

How Do AI Agents Work? The Short Answer

AI agents work through a continuous cycle of reasoning and action:

Goal → context → plan → tool call → observation → adjustment → completion

The agent starts with an objective rather than a rigid sequence of clicks. It examines the information available, decides what should happen next, and calls the relevant tool. That tool might search a CRM, query a database, open a webpage, run code, or update a business record.

The tool returns an observation: data, confirmation, an error, or an unexpected result. The agent adds that observation to its working context and chooses the next action. The loop continues until the task reaches its completion criteria or requires human input.

Consider a request to find neglected sales opportunities. The agent may:

1. Query the CRM for open deals.
2. Filter records with no recent interaction.
3. Retrieve the latest notes and stakeholders.
4. classify the likely blocker.
5. draft a next step for each account.
6. request approval before updating the pipeline.

A traditional automation follows a predefined route and fails when the input falls outside its rules. An AI agent can interpret variable inputs, revise its plan, and select a different action while staying inside its permissions.

How the AI Agent Loop Works in 7 Steps

The agent loop is not a fixed workflow drawn in advance. The model selects an action, observes what happened, and uses the new state to determine the next move.

1. The Agent Receives a Goal

A task can begin with a prompt, scheduled event, application trigger, system alert, or handoff from another agent. The request should define the expected outcome rather than every intermediate action.

“Find enterprise deals at risk this quarter and prepare an intervention plan.”

The agent also receives operating instructions: available tools, data boundaries, business rules, approval requirements, and completion criteria.

2. It Retrieves the Relevant Context

The model cannot make a reliable decision from the goal alone. It gathers the information required for the current step from sources such as:

• Conversation history
• CRM records
• Emails and meeting notes
• Internal documentation
• Databases
• Connected applications
• Real-time system events

For the at-risk deal request, the agent may retrieve renewal dates, deal values, recent interactions, open support cases, stakeholders, and recorded next steps.

3. It Breaks the Goal Into Actions

The reasoning model assesses the current state and creates a working plan. It may decide to identify eligible deals first, retrieve account context second, classify risk third, and prepare recommendations last.

This plan can change. If one deal has no recent notes, the agent might inspect email history. If the account lacks an owner, it may flag the missing field rather than attempt an unauthorized assignment.

4. It Selects and Calls a Tool

The agent cannot access business systems through reasoning alone. It sends a structured request to an approved tool, such as:

• Search CRM records
• Query a database
• Read a document
• Browse a webpage
• Execute code
• Create a task
• Update a pipeline field

A CRM tool call might include a pipeline, deal status, minimum value, renewal window, and inactivity threshold as structured arguments.

5. It Observes the Result

Every tool call returns new information. The response may contain the requested records, partial data, a permission error, an empty result, or confirmation that an action succeeded.

The model adds that observation to its working context and evaluates it against the goal. If the query returns too many deals, the agent can refine the criteria. If a required field is missing, it can search another approved source or report the gap.

6. It Applies Guardrails and Requests Approval

Guardrails check whether the proposed action stays within policy. They can validate tool arguments, restrict accessible records, enforce structured outputs, mask sensitive data, or pause before a consequential action.

The agent may analyze every open deal autonomously but require confirmation before it:

• Sends an external message
• Changes deal ownership
• Edits records in bulk
• Deletes information
• Commits spending
• Shares confidential data

Approval separates autonomous preparation from authorized execution.

7. It Completes, Escalates, or Hands Off

The loop ends when the agent satisfies its completion criteria. It returns the analysis, updates the approved system, and records what happened.

If the task falls outside its tools or permissions, the agent can escalate with the context already collected. In a multi-agent system, it may instead hand the task to a specialist agent while preserving the relevant state.

The result is controlled autonomy: the agent chooses how to reach the goal, but instructions, permissions, guardrails, and completion criteria define the operating boundary.

All The Components Behind an AI Agent

An AI agent is not a language model acting alone. It combines a reasoning engine with instructions, business context, tools, memory, orchestration, and guardrails. Each component solves a different part of the execution problem.

How AI Agents Use Tools to Take Action

A language model can reason about a task, but it cannot inspect a live pipeline, edit a record, or run a calculation without tools. Tools expose specific external capabilities that the agent can call through structured requests.

The process follows four steps:

1. The model identifies the action required.
2. It selects an available tool.
3. It generates the arguments expected by that tool.
4. It reads the result and decides what to do next.

For example, an agent investigating inactive opportunities might call a CRM search tool with structured arguments such as:

{

 "pipeline": "New Business",

 "status": "Open",

 "minimum_value": 20000,

 "days_since_last_interaction": 30

}

The CRM returns matching deals. The model then evaluates those records and may call another tool to retrieve notes, identify stakeholders, or update an approved field.

APIs for Structured System Operations

APIs expose predefined application functions such as retrieving records, creating tasks, updating fields, or processing transactions. They work well when the request and response follow a stable schema.

The agent does not operate the application interface. It sends a machine-readable request directly to the system and receives structured data in return.

MCP Servers for Standardized Tool and Data Access

Model Context Protocol gives compatible AI clients a consistent way to connect with external tools and data. Instead of building a separate integration for every model and application combination, an MCP server publishes the capabilities an agent can use.

folk MCP, for example, lets ChatGPT, Claude, Cursor, VS Code, and other compatible clients search and update a folk workspace through one hosted server. The agent can work with people, companies, deals, notes, groups, and interaction history according to the authenticated user’s permissions.

Browsers for Interface-Based Work

Some systems do not expose the required API or MCP tool. A browser agent can navigate pages, read interface elements, enter data, click controls, and inspect the resulting state.

Browser use provides broad access but introduces more variability. Layout changes, pop-ups, login requirements, and ambiguous controls can interrupt the workflow. Sensitive or irreversible actions usually require confirmation before the final click.

Code Execution for Analysis and Transformation

Code execution helps agents process data, test assumptions, transform files, run simulations, or validate calculations. A data agent might write Python to merge CSV files and identify outliers. A coding agent might run a test suite after editing the repository.

The output returns to the agent as another observation. A failed test, empty query, or validation error does not automatically end the task. It gives the model new context for the next step in the loop.

How Memory and Context Work in AI Agents?

An agent needs enough state to understand what has already happened, what remains unresolved, and which information matters for the next action. That state can live inside the current context window, an external memory store, or the business system where the work takes place.

Working Context for the Current Task

Working context contains the information available during the active run:

• Original goal
• System instructions
• Recent messages
• Tool definitions
• Retrieved records
• Tool results
• Current plan
• Actions already completed

The context window has a finite capacity. Long-running agents cannot keep every raw document, message, and tool output active indefinitely. They may summarize completed steps, remove irrelevant details, or retrieve information only when needed.

Stored Memory Across Sessions

Persistent memory preserves selected information after the current run ends. It can include user preferences, previous decisions, account-specific facts, unresolved tasks, or summaries of earlier work.

Useful memory remains selective. Storing every interaction creates noisy retrieval and increases the chance that outdated information affects a future decision. Production systems therefore need rules for what to retain, update, expire, or delete.

External Systems as Operational Memory

The most reliable memory often already exists in a business application. A CRM holds relationship history. A help desk stores previous incidents. A project tracker records owners, blockers, and status changes. A code repository preserves the current implementation.

An AI Sales Assistant connected to folk does not need to memorize an entire customer relationship inside the model. It can retrieve the latest contacts, notes, interactions, and deal state when the task begins, then write approved changes back to the CRM. folk remains the durable source of truth.

Memory Does Not Mean Continuous Model Training

Most AI agents do not update their underlying model weights after every task. They appear to remember because the system saves information externally and retrieves it during a later run.

Feedback can improve future behavior through corrected records, revised instructions, better tool descriptions, approved examples, or updated memory. Actual model training remains a separate process. This distinction matters because stored context can be inspected, corrected, and deleted more easily than knowledge embedded in model weights.

How an AI Sales Assistant Works With folk CRM

An AI Sales Assistant becomes operational when it can access live relationship data and write approved results back to the CRM. folk provides that connection through a hosted MCP server compatible with ChatGPT, Claude, Cursor, VS Code, and other MCP clients.

Consider the following task:

“Find open deals above $20,000 with no interaction in the last 30 days, identify the likely blocker, and prepare a recovery plan.”

1. Authenticate the AI Client

The user connects the AI client to folk through secure OAuth. The agent receives the same workspace access as the authenticated folk account, based on its existing role and permissions.

2. Translate the Goal Into Search Criteria

The agent extracts the operational conditions from the request:

• Deal status: open
• Minimum value: $20,000
• Inactivity period: 30 days
• Required output: blocker and recovery plan

It then selects the relevant folk search tools.

3. Retrieve Live Deal Data

Through folk MCP, the agent searches the workspace for matching opportunities. The result can include the deal name, company, owner, value, current stage, and last interaction date.

This first result identifies the affected records but does not provide enough context to diagnose the problem.

4. Reconstruct Each Relationship

The agent retrieves the people, companies, notes, custom fields, and interaction history associated with each deal. It looks for signals such as:

• Unanswered follow-ups
• Pricing objections
• Missing decision-makers
• Procurement delays
• Product requirements
• Absent next steps
• Declining engagement

The CRM acts as operational memory. The agent works from the relationship history already stored in folk instead of relying on context copied into the prompt.

5. Classify the Blocker

The reasoning model compares the evidence and assigns a likely cause to each stalled opportunity. It should distinguish confirmed facts from inference.

Por ejemplo:

• Confirmed: The buyer requested an SSO security review.
• Inferred blocker: The deal has stalled because no security owner was added after that request.
• Missing evidence: No interaction confirms whether budget approval is complete.

This separation prevents a plausible interpretation from being written back as fact.

6. Prepare the Recovery Plan

The agent recommends an account-specific next action rather than applying one generic follow-up sequence:

• Introduce a technical stakeholder
• Send the requested security documentation
• Re-engage the economic buyer
• Confirm the procurement timeline
• Schedule a decision call
• Close an opportunity with no credible path forward

It can also draft a contextual message based on the recorded conversation and current deal stage.

7. Request Approval

Search, analysis, and drafting can run without changing the workspace. Before editing a deal, sending a message, or creating several tasks, the agent presents the proposed actions for review.

The approval step might display:

“Move Northstar to Security Review, add ‘Introduce solutions engineer’ as the next step, and save the recovery summary to the deal notes?”

8. Write the Approved Result Back to folk

After confirmation, the agent calls the relevant folk tools to update the deal, add a note, change a field, or record the next action. It then checks the tool response to confirm that the update succeeded.

The complete loop stays connected: folk supplies the relationship context, the AI assistant interprets it, and folk stores the approved outcome. No separate database or custom middleware needs to sit between analysis and execution.

👉 Try folk CRM for free and connect an AI Sales Assistant to the live context behind every deal.

Conclusión

AI agents work by repeating a simple loop: interpret a goal, retrieve context, choose a tool, take action, and evaluate the result. Models provide the reasoning, while tools, memory, orchestration, and guardrails turn that reasoning into reliable business execution.

For sales teams, folk CRM gives AI agents the live relationship context required to act with relevance. Through MCP, an AI Sales Assistant can investigate deals, prepare next steps, and update approved records without separating the decision from the workflow.

Preguntas frecuentes

How do AI agents work in simple terms?

AI agents receive a goal, gather relevant context, plan the next action, use a tool, inspect the result, and repeat the process until the task is complete or requires human input.

What tools can an AI agent use?

AI agents can use APIs, MCP servers, databases, browsers, code execution environments, CRMs, help desks, calendars, email platforms, and other authorized business systems.

Do AI agents learn from every task?

Most agents do not retrain their underlying model after each task. They preserve useful information through conversation state, external memory, databases, files, and business systems such as a CRM.

What is the difference between a single agent and a multi-agent system?

A single agent controls the full workflow and calls several tools. A multi-agent system divides the work among specialists coordinated by a manager agent or direct handoffs.

How does an AI Sales Assistant work with a CRM?

An AI Sales Assistant retrieves live customer and pipeline context, analyzes the account, recommends the next action, requests approval when required, and writes the approved result back to the CRM.