Agentic Workflows
Multi-step AI task orchestration where an agent plans, executes tools, and verifies results to accomplish complex goals.
Agentic workflows are a design pattern for orchestrating complex AI tasks that require multiple steps, decision-making, and tool usage. Unlike simple prompt-response interactions, agentic workflows give the AI model autonomy to plan, execute, and verify its own work.
The core pattern follows a three-phase cycle: Planning (breaking down the goal into steps), Execution (using tools and making API calls to accomplish each step), and Verification (checking if the goal was achieved and deciding whether to continue, retry, or conclude).
For example, if asked to 'research competitors and create a report,' an agentic workflow might: (1) search the web for competitors, (2) visit their websites to extract key information, (3) organize findings into structured data, (4) generate a formatted report, and (5) verify all claims have citations.
Agentic workflows differ from simple prompting in their autonomy and multi-turn nature. They can adapt their plan based on intermediate results, handle failures gracefully, and work toward goals that require dozens of steps. However, this autonomy comes with increased cost (multiple LLM calls), latency (sequential operations), and risk (agents can take unexpected actions).
Common architectures include: ReAct (Reasoning + Acting, where the model alternates between thinking and tool use), Chain-of-Thought planning (generate full plan upfront, then execute), and Graph-based workflows (state machines with conditional branching). Frameworks like LangGraph, CrewAI, and AutoGen implement these patterns, but they can also be built with simple loops and tool calling APIs.
Use agentic workflows when: (1) tasks require multiple tools or API calls, (2) the sequence of steps depends on intermediate results, (3) you need error recovery and retries, or (4) the task is too complex to specify procedurally. Avoid when: (1) a single prompt suffices, (2) cost/latency is critical, (3) determinism is required, or (4) you lack proper guardrails for autonomous actions.
Key Takeaways
- Agentic workflows orchestrate multi-step AI tasks through planning, execution, and verification cycles
- Agents can use tools, adapt plans based on results, and handle failures autonomously
- Common patterns include ReAct (reasoning + acting), chain-of-thought planning, and graph-based state machines
- Frameworks like LangGraph and CrewAI implement these patterns, but custom solutions offer more control
- Use for complex multi-step tasks; avoid when simple prompting suffices or determinism is critical
- Requires proper guardrails to prevent runaway costs, infinite loops, and unintended actions
In This Platform
This platform's build process uses a lightweight agentic pattern: it scans content files, identifies missing cross-references, and generates validation reports with actionable recommendations. The 'validate' command plans which checks to run, executes them, and produces a summary—demonstrating planning, execution, and verification.
- build.js