Agentic Workflows: Design Patterns for Autonomous AI Systems

Most software workflows are pipelines: input in, steps execute in order, output out. They're predictable, debuggable, and brittle. When a step fails — an API is down, a page layout changed, the data doesn't look like you expected — the workflow stops and waits for a human.

Agentic workflows change this. Instead of a fixed sequence of steps, you give an AI agent a goal and let it decide how to get there — adapting in real time based on what it finds. The shift isn't just technical; it changes what's possible to automate.

This guide covers the design patterns, decision frameworks, and capability requirements for building agentic workflows that actually work in production.

What Makes a Workflow "Agentic"?

A workflow becomes agentic when it delegates decisions to the AI rather than pre-scripting every branch. The key word is autonomy.

The core insight from Andrew Ng's work on agentic design: an agentic workflow isn't a better pipeline. It's a different category of automation — one where the system makes choices during execution.

The Four Patterns of Agentic Workflows

Every agentic workflow is built from four fundamental patterns, used individually or in combination.

Pattern 1: Reflection

The agent produces output, then critiques its own work and improves it.

Write code → Review the code → Find bugs → Fix them → Review again

This is the simplest pattern and often the highest-ROI. Even a basic "review your own work and improve it" loop catches errors that a single-pass generation would miss. LLMs are better at critiquing output than producing perfect output on the first try — reflection harnesses that asymmetry.

Pattern 2: Tool Use

The agent invokes external tools to gather information or take action beyond text generation.

"What's the current price of X?" → call search tool → "The price is $Y" → continue

Tools turn an agent from a reasoning engine into an actor. Without tools, the agent can only think. With tools — search, crawl, generate, store, publish — it can affect the world.

This is where AnyCap becomes the agent's capability layer. Instead of the agent saying "I wish I could search the web," it runs:

anycap search --prompt "What is the current price of NVIDIA stock?"

The tool executes. The agent reads the result. The workflow continues.

Pattern 3: Planning

The agent breaks a complex goal into sub-tasks, executes them in order, and adjusts the plan as it learns.

Goal: "Write a market report on AI video"
→ Plan: (1) Search for key players (2) Crawl their pricing pages
  (3) Compare features (4) Write report (5) Publish
→ Execute step 1 → Discover new player → Revise plan → Continue

Planning is where agentic workflows diverge most dramatically from traditional pipelines. A pipeline has a fixed plan. An agentic workflow has a plan that evolves based on what the agent discovers during execution.

Pattern 4: Multi-Agent Collaboration

Multiple agents with different specialties work on different parts of a task, coordinated by an orchestrator.

Research Agent: finds sources
Writer Agent: produces the report
Reviewer Agent: checks for errors and gaps
Publisher Agent: deploys the final page

Multi-agent systems add complexity but enable specialization. A research agent can be optimized for thoroughness while a writer agent is optimized for clarity — different system prompts, different tools, different priorities.

Capabilities: What Your Agent Needs to Execute Workflows

A workflow pattern without tools is just a diagram. The agent needs actual capabilities to execute:

The quality of an agentic workflow is directly proportional to the quality of the tools available to it. An agent with only a search tool produces search results. An agent with search + crawl + generate + store + publish produces finished, delivered work.

The Orchestration Decision: When to Use Which Pattern

Not every task needs a multi-agent planning system. The decision framework:

Is the task path predictable?
  → Yes: Traditional pipeline is fine. Don't over-engineer.
  → No: Use Tool Use or Planning pattern.

Does the task benefit from self-critique?
  → Yes: Add Reflection.
  → No: Skip it.

Is the task too large for one agent?
  → Yes: Consider Multi-Agent.
  → No: One agent is simpler and more reliable.

The most common mistake: jumping to multi-agent before exhausting what a single well-tooled agent can do.

Production Considerations

Cost Management

Agentic workflows can be expensive. Every tool call costs credits; every planning step burns tokens. Mitigations:

• Cap the number of steps per workflow execution
• Use cheaper models for simple subtasks (reflection, formatting)
• Cache common tool results (don't search for the same thing twice)

Failure Handling

Agentic workflows fail differently than pipelines. A pipeline fails at a specific step with a specific error. An agentic workflow might go down a wrong path for several steps before realizing the error.

Design for this:

• Timeouts: If the workflow exceeds N steps or T minutes, return partial results
• Checkpoints: Save intermediate state so the agent can resume, not restart
• Human-in-the-loop: For high-stakes actions (publishing, sending), require approval

Observability

You can't debug what you can't see. Log every decision: what tool was called, with what parameters, what result came back, and what the agent decided to do next. Without this, you're debugging a black box.

From Theory to Practice

Agentic workflows are not a future concept. They're running in production today — automating research, generating content, managing data pipelines, and delivering finished work.

The barrier isn't the patterns. It's the tool access. The patterns are well-documented. What's been missing is a unified way for agents to actually execute them — to search, crawl, generate, store, and publish without integrating a dozen separate APIs.

AnyCap provides that unified capability layer. One CLI. Every tool. The agent focuses on decisions; the runtime handles execution.