Agentic Workflows
Design patterns where AI agents execute complex multi-step tasks autonomously, combining reasoning, tool use, and iterative decision-making.
What it is
An agentic workflow is a pattern where one or more AI agents execute complex tasks autonomously, making decisions at each step about what action to take, which tool to use, and when to request human intervention. Unlike a fixed pipeline, the agent adapts its behavior based on intermediate results.
Fundamental patterns
Reflection
The agent evaluates its own output and improves it iteratively:
Generate → Evaluate → Refine → Evaluate → Deliver
Useful for: writing, code generation, analysis.
Planning (Plan-and-Execute)
The agent decomposes the task into subtasks before executing:
Analyze task → Create plan → Execute step 1 → ... → Step N → Synthesize
Useful for: research, complex tasks with multiple dependencies.
Tool use (ReAct)
The agent alternates between reasoning and action:
Think → Act → Observe → Think → Act → Observe → Respond
This is the most common pattern in frameworks like Strands Agents.
Multi-agent
Multiple specialized agents collaborate:
Orchestrator → Research agent → Writer agent → Reviewer agent → Result
Autonomy levels
- Assisted: agent suggests, human approves each step
- Supervised: agent executes, human reviews key points
- Autonomous: agent executes end-to-end, reports results
- Collaborative: multiple agents coordinate with minimal human intervention
Design considerations
- Guardrails: define clear boundaries of what the agent can and cannot do
- Observability: log every decision and action for debugging
- Error recovery: the agent should handle failures gracefully
- Cost: each iteration consumes tokens — establish limits
- Evaluation: measure result quality, not just completion
Connection with MCP
The Model Context Protocol provides the tool layer that agentic workflows need. Without a standard protocol for discovering and using tools, each workflow requires ad-hoc integrations.
Why it matters
Agentic workflows allow LLMs to move from answering questions to executing complex multi-step tasks. Understanding their patterns — ReAct, planning, reflection — is the difference between building chatbots and building assistants that actually complete work.
References
- The Landscape of Emerging AI Agent Architectures — Masterman et al., 2024.
- Building Effective Agents — Anthropic, 2024. Practical guide to agentic patterns.
- Design Patterns for AI Agents — Zhuge et al., 2024. Taxonomy of design patterns for agents.