AI Agents
See what AI agents can do for your industry or process: the jobs they take on, how each agent runs, and a worked example with sample data.
Direct Answer
AI agents are software given a goal instead of a fixed script: they plan steps, call tools, check the results, and stop or hand off to a person when a decision carries risk. Pick your industry or process below to see what an agent can do, how it runs, and a worked example.
AI Agents for Code Review in Software and SaaS
AI agents for code review in software and SaaS demonstrate goal-driven analysis, tool use, and handoffs to produce actionable review results and maintain code quality.
AI Agents for Code Review in Software Engineering
This reference shows how an AI agent tackles code review tasks, plans steps, calls tools, evaluates results, and hands off when appropriate, with a concrete worked example.
AI Agents for Code Review
This reference shows how an AI agent performs code review work, from goal formulation through tool use to final decision, with a concrete worked example.
AI Agents for Collections and Recovery in Banking
This page documents how a goal-driven AI agent handles collections and recovery tasks, the tools it uses, and a concrete worked example from goal to finish.
AI Agents for Collections and Recovery
This page shows how AI agents execute collections and recovery goals, the loop they run, required tools, and a concrete worked example from goal to finish.
AI Agents for Collections in Finance
This page shows how an AI agent handles debt collection tasks, plans steps, calls tools, and hands off when needed, with a concrete worked example.
AI Agents for Collections
This page defines ai agents for collections, detailing the goal-driven loop, required tools, and a concrete worked example that shows planning, tool use, and handoffs.
AI Agents for Commercial Analytics in Pharma and Life Sciences
A practical reference showing how AI agents execute commercial analytics in pharma and life sciences, from goal to finish, with a concrete worked example.
AI Agents for Commercial Analytics
This page defines the AI agent loop for commercial analytics, detailing goals, planning, tool calls, checks, and handoffs with a concrete worked example.
AI Agents for Commodity Price Forecasting in Agriculture
This page shows how an AI agent for commodity price forecasting in agriculture operates, from goal to tool use, with a worked example and concrete data fields for reproducibility.
AI Agents for Commodity Price Forecasting
This page shows how AI agents forecast commodity prices by forming goals, planning steps, calling data tools, and validating results, with a concrete worked example.
AI Agents for Commodity Trading in Oil and Gas
Shows how an AI agent plans oil and gas trades, calls data tools, validates results, and finishes or hands off in a commodity market, with a concrete worked example.
AI Agents for Commodity Trading
This page shows how a commodity trading AI agent sets a trading goal, plans steps, calls tools, checks results, and hands off when appropriate.
AI Agents for Compensation Benchmarking in Human Resources
This page defines a practical AI agent loop for compensation benchmarking in human resources, detailing goals, tools, stop criteria, and a worked example.
AI Agents for Compensation Benchmarking
This reference shows how an AI agent performs compensation benchmarking tasks, plans steps, calls tools, checks outcomes, and hands off when needed, with a concrete worked example of a benchmark run.
AI Agents for Competitive Analysis in Product Management
Shows how an AI agent conducts competitive analysis in product management with a concrete worked example and required controls.
AI Agents for Competitive Analysis
This page explains how AI agents perform competitive analysis, including goal framing, planning, tool use, monitoring results, and handoffs, with a concrete worked example.
AI Agents for Competitive Benchmarking in Corporate Strategy
This page shows how AI agents perform competitive benchmarking in corporate strategy, detailing the goal-driven loop, required tools, guardrails, and a concrete worked example from goal to finish.
AI Agents for Competitive Benchmarking
This page shows how AI agents execute a goal-driven competitive benchmarking task, plan actions, call tools, verify results, and hand off when complete, with a concrete worked example to illustrate the loop.
AI Agents for Competitive Intelligence in Marketing
Shows how an AI agent conducts competitive intelligence work for marketing, from goal setting to tool use, result validation, and hand-off, with a concrete worked example.
AI Agents for Competitive Intelligence
This page defines how AI agents perform competitive intelligence tasks, how they plan, execute tools, verify results, and hand off when appropriate, with a contained worked example.
AI Agents for Complaint Resolution in Customer Service
A practical reference showing how AI agents handle complaint resolution in customer service, from goal to finish, with a concrete worked example and extended operational detail.
AI Agents for Complaint Resolution
This page shows how an AI agent handles complaint resolution from goal to hand-off, detailing the loop, required tools, and a concrete worked example.
AI Agents for Compliance Automation in Fintech
This page details how AI agents execute fintech compliance tasks, outlining goal-driven loops, required tools, stop criteria, and a concrete worked example to guide implementation and governance for organizations.
FAQ
What is an AI agent?
An AI agent is software given a goal rather than a fixed script. It plans steps, calls tools, checks the results, and either finishes or hands off to a person.
What is the difference between an AI agent and AI automation?
AI automation runs a defined process with AI at certain steps. An AI agent chooses its own next step toward a goal, which suits work where the path changes from case to case.
Which AI agent should a business build first?
Start with one narrow goal, read-mostly tool access, and a clear owner. Widen what the agent may do alone only after its logged runs show it can be trusted.