What are AI agents?

AI agents are software systems that don’t just chat—they plan, act, and iterate toward a goal. They use an LLM for reasoning (see LLMs) and combine it with memory, retrieval, and tool use (APIs, apps, databases, RPA) so they can break a request into steps, call the right systems, check results, and continue or hand off.

This “agentic AI” pattern is different from a simple chatbot: instead of drafting a reply once, an autonomous agent runs a loop of plan → act → observe → adjust until the job is done or a policy stops it.

Under the hood, an agent holds a task state, decides the next action, executes one or more tools, reads the outcome, and updates its plan. Grounding keeps it factual (documents, knowledge bases, business rules), while orchestration decides which tools are allowed and in what order. Standards like MCP help connect agents to tools in a consistent, auditable way so you don’t hard-wire integrations for every use case.

In enterprise settings, the difference between a cool demo and a dependable assistant is governance. Production agents run with permissions, budgets, rate limits, and audit logs; they expose their steps, capture evidence, and follow approval flows for risky actions.

Reliability comes from guardrails (allowed actions, schema checks), evaluation on real tasks, and a clear human-in-the-loop path for exceptions. You measure outcomes like task-success rate, time-to-resolution, quality scores, and cost per resolution—not just click or reply metrics.

Where they fit best

1. Customer service flows that span systems (identify, troubleshoot, create case, issue refund, notify).
2. Commerce and catalog ops (reconcile product data, fix broken variants, enrich attributes, close loops with merchants).
3. Sales and RevOps (research an account, draft outreach, log calls, update CRM with evidence and next steps).
4. IT and finance back office (access requests, user provisioning, invoice checks, compliance notes with citations).

Example

A retail “exchange agent” takes: “Swap order #4831 to size 42, keep color black.” It verifies the order, checks stock, calls the pricing service for any difference, creates an exchange in the OMS, generates a return label, and emails the customer. If size 42 is out of stock, it proposes alternatives that match the shopper’s price band and support needs, backed by catalog attribute extraction and reviews. Every step is logged, and refunds above a threshold route to a human approver. This is how an agent moves from words to completed work—safely and visibly.

Good agents depend on trustworthy data and evaluation. Teams often pair them with high-quality labeled datasets and repeatable annotation workflows to train intent detection, validate outputs, and monitor drift. That’s where Taskmonk’s platform and managed services help: clean labels, maker–checker reviews, and golden sets make agent actions auditable and improvements measurable.

‍