Autonomous Agents
AI agents that think and adapt to complete complex tasks independently
Give AI agents a goal and let them figure out how to reach it -- researching, using tools, and adjusting strategy as they work.
What It Does
Autonomous Agents think through complex problems and adapt their approach rather than following predefined steps. You provide the objective; the agent decides how to accomplish it. Agents can search for information, call integrations, make judgment calls, and revise their strategy based on what they find.
Key Benefits
- Automate tasks that require flexible reasoning, not rigid if-then logic
- Cover more ground with less configuration -- describe the goal, not every step
- Control what data and systems each agent can access
- Add human approval gates so you review outputs before they take effect
- Refine agent behavior over time through feedback
Knowledge Maps
Agents use Knowledge Maps to navigate your business context. Instead of loading every capability upfront, an agent learns what knowledge exists (CRM, brand guidelines, policies, integrations) and loads only what it needs for the current task. This keeps costs low and focus high. Loading "CRM knowledge" gives the agent both instructions on how to use it and the actual tools to query data.
How Agents and Workflows Relate
Workflows define structure; agents supply reasoning. An agent can be invoked as a step inside a workflow, or an agent can trigger a workflow as a follow-on action. The two features compose naturally.
Common Use Cases
- Competitive research -- receive a list of competitors, visit sites, extract key data, and produce a comparison report
- Customer support triage -- read an inquiry, check account context, classify urgency, and draft a response for human review
- Content generation -- research a topic brief, identify key points, and draft an article ready for editing
Last Updated: 2026-05-22