AI Agents
What Is an AI Agent?A practical definition.
AI agents are software systems that can carry out multi step work on a user’s behalf with less back and forth than a typical chatbot.
Definition
An AI agent is a system designed to pursue a goal.
It combines a model with tools (APIs, data sources, business apps), clear permissions, and guardrails so it can gather context, make decisions, and take actions safely.
Boundaries
Agents vs workflows
Workflows are mostly predefined while agents decide the sequence of steps dynamically. In practice, many “agent” deployments are workflows with a few agent-like steps, which is often the right place to start.
Workflow
The steps are mostly predefined. The model fills in specific parts like summaries, classification, drafting, or extraction.
Agent
The model can dynamically decide the sequence of steps and tool calls as it goes, based on what it finds along the way.
What an agent is not
Single turn Q&A chatbots, simple RAG answerers, and classifiers that do not control a task loop.
Agent Loop
How do AI agents work?
Most agents follow a loop that clarifies the goal, gathers context, plans next actions, and validates results.
Architecture
Core building blocks of an AI agent
Practical agents are systems, not just prompts. These components make them reliable and auditable in production.
Model
The reasoning and decision engine, often an LLM paired with smaller models for specific tasks.
Instructions and role
Operating rules: goal, boundaries, tone, definitions of success, and how to behave when uncertain.
Tools
Allowed actions and information sources, exposed through function calls or APIs.
State (optional memory)
Short term state for the current run and optional longer term memory when the use case truly needs it.
Guardrails
Permissions, tool allow lists, policy checks, output validation, and approval gates for risky actions.
Orchestration and monitoring
The agent loop, timeouts, retries, logging, tracing, and the ability to hand control back to a human.
Evaluation
Tests and scorecards that measure success, catch regressions, and keep the system improving over time.
Outcomes
What do AI agents do?
Agents are useful when work requires multiple steps across tools and the steps cannot be fully scripted ahead of time.
Automate repeatable business processes
Run routine tasks end to end while keeping humans in control of key decisions.
Assist teams with analysis and decisions
Pull information together, summarize tradeoffs, and draft recommendations.
Execute actions inside business systems
Create tickets, update CRM records, generate reports, reconcile data, schedule follow ups, or trigger workflows.
Handle edge cases with escalation
When the situation is unclear, the agent can ask for clarification or route to a person instead of guessing.
Example
Finance agent with approval gates
A controller or internal audit team can use an agent to reconcile revenue, validate compliance, and draft review-ready schedules while keeping approvals in human hands.
Spectrum
Types of AI agents
Agents exist on a spectrum. The right choice depends on risk, complexity, and how much autonomy you actually need.
Assistant embedded in a workflow
Helps with parts of a process, but the overall path is mostly predefined.
Tool using agent
Chooses the next tool call dynamically based on intermediate results.
Multi agent system
Specialized agents handle different roles like research, planning, execution, and QA with handoffs.
Long running agent
Operates over longer periods with checkpoints, monitoring, and strong governance.
Playbook
How to get value from AI agents
Teams typically succeed with agents by doing a few simple things well.
Outlook
Are AI agents the future?
Agents are already delivering value in customer support, operations, analytics, finance, and software delivery. The biggest gains come from clear goals, the right tools, strong guardrails, and humans staying in control of high impact decisions.