Answer: An AI agent is software that can interpret an objective, use tools and take multiple steps toward an outcome. The useful question is not whether it is called an agent; it is exactly what authority, tools and data it has.

What this means in practice

Research Agents

A research agent gathers and organizes information on companies, projects or opportunities. Read-only access keeps the risk low.

Document-Processing Agents

These agents classify, extract and summarize documents such as bid packages, RFIs and submittals for a person to review.

Follow-Up Agents

A follow-up agent tracks open items and prepares routine reminders through approved channels, escalating anything unusual.

Reporting Agents

A reporting agent pulls data from several systems into summaries and exception lists. It only works when the source systems are reliable.

Workflow Coordinators

A coordinator moves work between steps, tools and people and keeps track of state, so nothing is dropped between handoffs.

Multi-Step Operational Assistants

Agents that take several actions toward a goal need explicit authority, limits, logs and recovery, because flexibility adds governance burden.

A practical construction test

  1. Define the business outcome.
  2. Measure how the workflow performs today.
  3. Identify what AI is being asked to interpret or decide.
  4. Define what remains under human authority.
  5. Test with real examples and acceptance criteria.
  6. Measure results before expanding scale or autonomy.

Where governance fits

The AI model can change. The company’s operating rules should not change silently with it. Catha’s Governance Cage is designed to preserve the business authority layer above the model and workflow.