AI agent

How an AI agent works

IBM describes three stages that most AI agents share 1. First the agent receives a goal and the tools it may use, then it plans by breaking the goal into tasks and subtasks 1. Simple tasks may skip a formal plan and move straight to action. Complex goals need that decomposition so the model does not try to solve everything in one reply.

Second, the agent reasons with tools. When its own knowledge is not enough, it calls external datasets, APIs, searches or other agents, then updates its plan from what comes back 1. That tool calling step is what separates an agent from a chatbot that only replies from training data 1. Without tools, the system is stuck in the past. With tools, it can read a live weather feed, a CRM record or a B2B enrichment API.

Third, the agent learns from feedback. People, other agents or stored outcomes can correct the next attempt 1. IBM notes that developers, deployers and end users each shape the agent's rules and goals, so autonomy sits inside boundaries set by people 1. An agent that cannot be interrupted, logged or denied is a liability once it can send messages or spend money.

Frameworks package the loop differently. LangChain describes agents as dynamic systems that define their own process and tool usage, in contrast to workflows with fixed paths 2. An agentic workflow often wires several of those agents into a larger process with checkpoints. Agents attach tools through host APIs or through Model Context Protocol, where a client discovers a server's tools and runs them on the agent's behalf.

Risk patterns repeat across demos. Infinite tool loops, shared failure modes in multi-agent stacks and high-impact actions without confirmation all show up in IBM's overview 1. Activity logs, interruptibility and human approval on consequential steps are the usual countermeasures 1.

What an AI agent needs to stay useful

Autonomy without constraints tends to loop or spend money. IBM's overview of agent risks points to infinite tool loops, multi-agent failure modes and actions that need confirmation before they run 1. Practical builds add a few controls before the first production goal.

• A clear goal and stop condition, so the agent knows when to finish or escalate to a person.
• A small tool catalog with descriptions that state cost and side effects up front.
• Logging of which tools ran and what they returned, so a person can audit the path later 1.
• Interruptibility, so a person can halt a runaway sequence without killing the whole host 1.
• Confirmation on high-impact actions such as mass email, refunds or money movement 1.

Data agents add one more rule. Enrichment and lookup tools bill per call. Waterfall finders that charge only on a hit keep empty records from draining the budget, which matters when an agent retries across a long list 3. RichAPI's email finder and phone finder follow that miss rule in the product catalog 3.

Memory helps personalization but raises privacy stakes. Store only what the next run needs, set retention limits and keep customer data out of prompts that leave the team's control 1. An agent that remembers everything forever is hard to govern and easy to leak.

Framework choice matters less than these boundaries. LangGraph, custom hosts and MCP clients can all run the same loop 2. What fails in production is usually missing stop rules, missing logs or a tool catalog that hides cost. The framework choice rarely is.

Example of an AI agent run

IBM's surfing-trip example shows the loop without sales jargon 1. A user asks which week next year will have the best weather for surfing in Greece. The agent cannot answer from training data alone, so it queries a weather history database 1. It still lacks surfing criteria, so it asks a specialist surfing agent and learns that high tides, sun and little rain are the target 1. Combining both results, it recommends a week to the user and can store the feedback for later runs 1.

A B2B data agent follows the same shape with credits on the line. Given a list of LinkedIn profiles and a goal to attach a work email, the agent calls an email finder tool for each row. RichAPI's email finder is a waterfall endpoint. A miss bills zero credits and the response lists every provider tried 3. The agent can then decide whether to try a phone finder, skip the row or hand the miss to a person, without paying for empty email results 3. New accounts start with 100 free credits and no card, which is enough to validate the loop end to end 3.

AI agent vs agentic workflow

An AI agent is the actor. An agentic workflow is the process that may include one or more agents. IBM defines an agent as a system that designs its own workflow with tools 1. The same publisher defines agentic workflows as multi-step processes where agents decide, act and coordinate with limited intervention 4. LangChain draws a related line. Agents choose their path. Workflows follow predetermined code paths 2.