Definition
Agentic Workflow
An agentic workflow is a process in which one or more AI agents carry out a multi-step task with some autonomy — planning, calling tools, and acting on results in a loop — rather than a human driving each step. The agent decides the next action toward a goal, within boundaries its operator sets, turning AI from a single-response assistant into a worker.
Key takeaways
- An agentic workflow is a multi-step task an AI agent carries out in a loop — plan, call a tool, read the result, repeat — rather than a human driving each step.
- The defining trait is autonomy: the system, not the human, chooses the next action toward a goal within set boundaries.
- They range from tightly scripted steps to open-ended planning to several specialized agents handing work off to each other.
- Autonomy is the value and the risk — keep reversible steps autonomous, gate consequential actions behind approval, and log everything.
A non-agentic AI interaction is a single turn: ask, receive an answer, done. An agentic workflow is a loop: the agent is given a goal, assesses the situation, picks a next step, takes it via a tool, reads the outcome, and repeats until the goal is met or a stop condition fires. That loop is what lets AI handle tasks no single prompt could — triaging an inbox, reconciling a report, working through a checklist of related changes.
Agentic workflows range in how much they decide for themselves. Some are tightly scripted — fixed steps with the model filling in judgment at each. Others are open-ended, letting the agent plan its own path. More ambitious setups orchestrate several specialized agents that hand work to each other. Across the spectrum, the defining trait is the same: the system, not the human, chooses the next action in pursuit of the goal.
Autonomy is the value and the risk. The more an agent can do without a human in the loop, the more leverage it provides — and the more important it becomes to bound its reach. The durable pattern keeps reversible, read-only steps autonomous while routing consequential actions through approval, and records every action so the workflow stays reviewable. Governance is what makes broad autonomy safe to grant.
Planoda runs agentic workflows where AI agents triage, draft, and execute across the workspace, with destructive actions gated by a propose/approve guardrail and every step written to the audit trail — so autonomy scales without bypassing oversight.
Related terms
- AI AgentAn AI agent is a software system that uses a large language model to pursue a goal across multiple steps — reading context, choosing tools, and taking actions — rather than answering a single prompt. In a work platform, agents triage issues, draft updates, and execute multi-step tasks as autonomous teammates, bounded by the permissions and approvals their operators set.
- Propose / Approve (AI Governance)Propose/approve is a governance pattern for autonomous software: instead of executing a consequential action directly, an AI agent emits it as a proposal that a human or policy must approve before it runs. It keeps fast, read-only work autonomous while gating destructive or irreversible operations — the practical way to give agents real power without surrendering control.
- Audit TrailAn audit trail is an append-only, time-ordered record of who did what, when, and to which object across a system. Every create, edit, delete, and approval is logged immutably, so any state can be traced back to the actions that produced it. Audit trails underpin accountability, debugging, compliance, and — increasingly — oversight of what AI agents do.
- MCP ServerAn MCP server implements the Model Context Protocol, an open standard that lets AI assistants connect to external tools and data through a uniform interface. Rather than building a bespoke integration per assistant, a tool exposes one MCP server describing its available actions and resources, and any MCP-capable AI can discover and use them safely.