Guide to AI agents
AI agents are digital workers that can carry out tasks, make decisions within defined boundaries and drive workflows across your systems. This guide explains what they are, how they differ from chatbots, and what it takes to make them work in production.
What is an AI agent?
An AI agent is software that takes in goals, plans steps, uses tools and reports results. Unlike a chatbot, the agent acts on the system, not just in the conversation.
Common components
Model
The generative core that interprets and plans.
Tools
APIs, databases, email, CRM and business systems the agent is allowed to use.
Memory
Short- and long-term memory so the agent can follow up over time.
Guardrails
Rules, access control and escalation to limit the damage from mistakes.
Observability
Logging and measurement so you know what the agent is actually doing.
Model
The generative core that interprets and plans.
Tools
APIs, databases, email, CRM and business systems the agent is allowed to use.
Memory
Short- and long-term memory so the agent can follow up over time.
Guardrails
Rules, access control and escalation to limit the damage from mistakes.
Observability
Logging and measurement so you know what the agent is actually doing.
Common use cases
- First-line support and case routing.
- Lead qualification and CRM updates.
- Compiling reports from multiple systems.
- Internal knowledge agent across policies and documentation.
- Document processing, extraction and validation.
What it takes to succeed
The most common reasons agent projects fail are an unclear definition of success, too broad a first version, and a lack of logging. We always start narrow, measure against real cases, and expand as quality is proven.
Where to go next
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