An AI system that autonomously plans, executes, observes, and iterates to complete complex goals.
An Agentic Workflow is a paradigm shift in AI system design. Instead of responding to a single prompt with a single output, an agentic system breaks down a complex goal into sub-tasks, executes them sequentially or in parallel, observes the results, and iterates until the objective is met. This mirrors how a human expert would approach a problem: plan, act, evaluate, adjust. Frameworks like LangChain and CrewAI enable developers to build these multi-step autonomous systems.
The agent receives a high-level objective and breaks it into manageable sub-tasks using an LLM-powered planner.
For each sub-task, the agent selects the appropriate tool (API call, database query, code execution) from its available toolkit.
The agent executes the action, observes the result, and determines if the sub-task was completed successfully.
If the result is unsatisfactory, the agent adjusts its approach and retries, learning from previous attempts.
Agents that handle multi-step support tickets by querying databases, checking order status, and drafting responses.
AI agents that write code, run tests, fix failures, and submit pull requests autonomously.
Agents that gather data from multiple sources, synthesize findings, and produce structured reports.
Knowing the definition is step one. Building it into your product is step two. That's where we come in.