encyclopedia
Unlike traditional chatbots that simply answer queries, AI agents operate with a degree of agency. They can break down complex instructions into a multi-step plan, use external tools (like web browsers, calculators, or APIs) to gather information, and adapt their approach based on the results. Agentic workflows transform AI from a passive knowledge retrieval system into an active digital worker.
Autonomous or semi-autonomous AI systems designed to achieve specific goals by planning, reasoning, and using tools to take actions in digital environments.
We help teams go from definitions to deployed workflows—safely and fast.
An effective AI agent requires more than just a language model. It relies on an architecture that combines understanding with action execution.
Agents use specific prompting frameworks to reason about tasks. Techniques like ReAct (Reason+Act) force the model to explicitly state its thought process ("I need to find X"), determine an action ("I will use the search tool"), observe the result, and decide the next step.
Because autonomous systems can drift off-task or make destructive errors when given API access, enterprise agentic workflows almost always incorporate "human-in-the-loop" approval gates for high-risk actions (like transferring funds or deploying code).
Yes. RPA strictly follows rigid, rule-based scripts. AI agents can handle ambiguity, recover from errors, and dynamically decide how to achieve a goal when the environment changes.
A multi-agent system involves several specialized AI agents collaborating on a task. For example, a "Researcher Agent" gathers data and passes it to a "Writer Agent" which is then reviewed by an "Editor Agent".
Get a copy of “AI Agents & Agentic Workflows” and an AI readiness checklist in your inbox.