AI agent
An AI agent is software that uses a language model to pursue a goal by choosing its own next steps: it reads the situation, picks a tool or action, checks the result, and repeats until the task is done or it needs a person. Unlike a chatbot, it acts inside your systems instead of only answering.
01why it matters for a business
Most AI a company has touched so far answers questions. An agent does work. That shifts the business question from which model is smartest to which multi-step processes can be handed over, with what permissions, and who checks the output. The value sits in the operational hours an agent absorbs: reconciling records, triaging requests, preparing documents, chasing missing information.
It also shifts the risk. An agent that can update a CRM, send an email, or issue a refund can make mistakes at machine speed. The companies that get value from agents treat them like a new hire with a narrow job description: limited access, clear escalation rules, and a log of everything they did. That framing makes it easier to decide what to automate first and how far to trust it.
02what it looks like in practice
Picture a distributor whose inside sales team spends mornings turning emailed purchase orders into orders in the ERP. An agent can read each email and attachment, match the customer and SKUs against the ERP through an API, flag anything ambiguous (an unknown part number, a price that does not match the contract), and create clean orders as drafts for a rep to approve. The rep's job moves from typing to reviewing exceptions.
The agent here is not one clever prompt. It is a model plus a set of tools (search customers, look up SKUs, create a draft order), a written policy for when to stop and ask, and evaluation tests run against past orders before it ever touches a live one.
03common mistakes
- Calling a chatbot an agent. If it cannot take an action in a system, it is an assistant, and the business case is different.
- Giving broad access on day one. Start with read access and draft-only writes, then widen permissions as the logs earn trust.
- Skipping the escalation path. Every agent needs a defined point where it stops and hands the case to a named person, with context attached.
- Measuring demos instead of outcomes. Track cases handled end to end, error rate, and time per case against the manual baseline.
04related terms
- Agentic workflowAn agentic workflow is a business process in which an AI model handles some of the steps itself, deciding how to complete them, while the overall sequence, rules, and handoffs are defined in advance.
- Tool use (function calling)Tool use, also called function calling, is the ability of a language model to request that your software run a specific function, such as looking up an order, querying a database, or sending a message, with structured inputs the model fills in.
- Multi-agent systemA multi-agent system is a setup in which several AI agents, each with its own role, instructions, and tools, work together on a task.
- Human in the loop (HITL)Human in the loop is a design pattern in which a person reviews, approves, or corrects an AI system's output at defined points before it takes effect.
- AI guardrailsGuardrails are the controls around an AI system that keep its inputs, outputs, and actions within acceptable limits: input filtering, output checks for policy, format, and sensitive data, limits on which tools and data it can reach, spending caps, and rules that route risky cases to a person.
05where insomnia club fits
Insomnia Club designs and builds AI agents that work inside the systems you already run, with permissions, escalation rules, and evaluation tests scoped up front and a fixed budget agreed before work starts.
see AI agent development →tell us what keeps you up at night.
Scoped by the people who ship it. Priced before we start.
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