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Agentic Workflow

TL;DR

An agentic workflow is a multi-step task executed autonomously by an AI agent using a loop of planning, tool calls, and self-evaluation. Unlike linear scripts, agentic workflows adapt — the agent decides each step based on prior results, replans on failure, and can spawn sub-agents for parallel work.

In depth

An agentic workflow replaces rigid scripts with autonomous LLM-driven execution. Instead of 'do A, then B, then C', the agent is given a goal and a toolbox, and it figures out the path. This adaptiveness is the defining feature — the workflow handles unexpected inputs, partial failures, and unknowns that break traditional automation.

Typical stages: **decompose** (break goal into sub-tasks), **delegate** (assign each sub-task to a tool or sub-agent), **execute** (call tools, observe results), **evaluate** (did we make progress?), **replan** (adjust strategy if stuck). Loop until the goal is met or a budget is exhausted.

Anthropic's published research (Building Effective Agents, 2024) outlines common patterns: **prompt chaining** (sequential steps), **routing** (classify then dispatch), **parallelization** (fan-out-fan-in), **orchestrator-workers** (manager agent + worker agents), **evaluator-optimizer** (generate + critique loop). Real systems combine these.

MCP is a natural fit for agentic workflows because MCP tools are the agent's action surface. Nanobot Enterprise, LangGraph, CrewAI, and AutoGen all wire MCP (or MCP-like) tools into their workflow engines.

Examples

  • 1
    Claude Code implementing a feature: plan → edit files → run tests → iterate
  • 2
    A competitive-intel agent: monitor sites → extract changes → summarize → post to Slack
  • 3
    A support triage agent: classify ticket → fetch history → draft reply → escalate if needed
  • 4
    A release agent: merge PR → deploy → verify → rollback on failure
  • 5
    Multi-agent research: CEO agent delegates to 3 researchers in parallel

What it's NOT

  • ✗An agentic workflow is NOT a fixed script — the agent decides each step dynamically.
  • ✗Agentic workflows are NOT necessarily multi-agent — a single agent can run a complex workflow.
  • ✗Not every task needs an agentic workflow — simple single-shot prompts are cheaper and faster.
  • ✗Agentic workflows are NOT infallible — always bound them with budgets and approvals.

Related terms

AI AgentTool UseModel Context Protocol (MCP)Function Calling

See also

  • Building Effective Agents (Anthropic)

Frequently asked questions

When should I use an agentic workflow?

For tasks with branching logic, uncertain inputs, or multi-step reasoning. Simple deterministic tasks should stay as scripts.

How do I bound cost?

Set token budgets, max iterations, and explicit goal-completion checks. Nanobot Enterprise uses the 'four laws' pattern (budget, scope, tools, time).

Can agentic workflows be tested?

Yes — build a fixed eval set of end-to-end scenarios. Measure success rate, cost, latency, and trajectory quality.

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