Agentic Workflows: What They Are and How to Build Them

An agentic workflow is a coordinated sequence of AI-driven steps designed to accomplish a complex, multi-stage goal. Think of it as a pipeline where AI agents handle the decision-making at each step.

The term “agentic workflow” is sometimes used interchangeably with “AI agent,” but there’s a useful distinction. A single agent is a loop: observe, think, act, repeat. An agentic workflow is a pipeline with multiple stages, each potentially involving one or more agents, with defined handoffs between stages.

Agent vs. Workflow

Concept	Agent	Agentic Workflow
Scope	Single goal, flexible steps	Multi-stage, coordinated process
Structure	Dynamic loop	Defined pipeline with checkpoints
Error handling	Self-correcting	Stage-level retries and fallbacks
Best for	Open-ended tasks	Repeatable business processes

A single agent is flexible but can be difficult to predict. An agentic workflow trades some flexibility for predictability — you know roughly what will happen at each stage, even if the agent at each stage makes dynamic decisions within that stage.

Anatomy of an Agentic Workflow

Input
  │
  ▼
[Stage 1: Data Gathering]  ← Agent with search tools
  │
  ▼
[Stage 2: Analysis]        ← Agent with code execution
  │
  ▼
[Stage 3: Human Review]    ← Human-in-the-loop checkpoint
  │
  ▼
[Stage 4: Output]          ← Agent formats and delivers result

Each stage has a well-defined input and output. Stage 1 receives the initial request and produces raw data. Stage 2 receives the raw data and produces an analysis. Stage 3 might be a human reviewing the analysis before anything is delivered. Stage 4 takes the approved analysis and formats it for delivery.

This structure makes workflows easier to debug than monolithic agents. When something goes wrong, you know which stage failed, what input it received, and what output it produced. With a single agent that runs for 50 steps, tracing the failure is much harder.

A Practical Example: Content Research Pipeline

Here’s a concrete example of an agentic workflow — a research pipeline that gathers and synthesizes content on a topic:

import asyncio
import anthropic

client = anthropic.Anthropic()

def gather_sources(topic: str) -> str:
    """Stage 1: Gather information about a topic."""
    response = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=2048,
        system="You are a research specialist. Provide detailed, factual information with clear sourcing.",
        messages=[{
            "role": "user",
            "content": f"Provide a comprehensive overview of: {topic}. Include key concepts, recent developments, and important technical details."
        }]
    )
    return response.content[0].text

def analyze_and_structure(raw_research: str, topic: str) -> str:
    """Stage 2: Analyze and structure the gathered information."""
    response = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=2048,
        system="You are a technical analyst. Extract key insights and structure them clearly.",
        messages=[{
            "role": "user",
            "content": f"Topic: {topic}\n\nRaw research:\n{raw_research}\n\nExtract and structure the 5 most important points, with a clear explanation of each."
        }]
    )
    return response.content[0].text

def write_summary(analysis: str, topic: str, audience: str = "developers") -> str:
    """Stage 3: Write a final summary for the target audience."""
    response = client.messages.create(
        model="claude-opus-4-6",
        max_tokens=1024,
        system=f"You write technical content for {audience}. Be precise, practical, and concise.",
        messages=[{
            "role": "user",
            "content": f"Write a 300-word summary about {topic} based on this analysis:\n\n{analysis}"
        }]
    )
    return response.content[0].text

def run_research_pipeline(topic: str) -> dict:
    """Run the full three-stage research pipeline."""
    print(f"Stage 1: Gathering sources for '{topic}'...")
    raw_data = gather_sources(topic)

    print("Stage 2: Analyzing and structuring...")
    analysis = analyze_and_structure(raw_data, topic)

    print("Stage 3: Writing final summary...")
    summary = write_summary(analysis, topic)

    return {
        "topic": topic,
        "raw_research": raw_data,
        "analysis": analysis,
        "summary": summary,
    }

result = run_research_pipeline("Model Context Protocol (MCP)")
print(result["summary"])

Notice that each stage is a separate function with a clear responsibility. The output of one stage feeds directly into the next. If Stage 2 fails, you can retry it with the same Stage 1 output without re-running Stage 1. This is a key advantage of pipeline-structured workflows.

Key Design Principles

1. Prefer deterministic code over LLM calls

Use Python for data processing, formatting, sorting, and anything that doesn’t require language understanding or reasoning. Reserve LLM calls for the parts that genuinely need them.

If you’re counting words, sorting a list, or transforming a JSON structure — don’t call an LLM. Do it in Python. LLM calls are expensive, slow, and non-deterministic. Use them only where their capabilities are essential.

2. Build in human checkpoints

For high-stakes workflows, add review gates before irreversible actions — sending emails, making payments, publishing content, modifying databases. A human checkpoint costs relatively little and prevents costly mistakes.

def human_checkpoint(output: str, stage: str) -> bool:
    """Ask a human to approve before proceeding."""
    print(f"\n--- {stage} Output ---")
    print(output)
    response = input("\nApprove? (y/n): ")
    return response.lower() == "y"

3. Design for failure

Each stage should be independently retryable. Store intermediate results so you don’t restart from scratch when something fails at Stage 4.

import json
from pathlib import Path

def save_checkpoint(stage: str, data: dict):
    path = Path(f"checkpoints/{stage}.json")
    path.parent.mkdir(exist_ok=True)
    path.write_text(json.dumps(data, indent=2))

def load_checkpoint(stage: str) -> dict | None:
    path = Path(f"checkpoints/{stage}.json")
    if path.exists():
        return json.loads(path.read_text())
    return None

4. Limit blast radius

Scope each agent’s permissions to the minimum needed for its stage. A research agent doesn’t need write access to your database. A formatting agent doesn’t need API keys for external services. Minimal permissions reduce the impact of agent errors.

5. Log everything

Each stage should log its input, output, and any errors. Without logs, debugging a multi-stage pipeline is very difficult. Include timestamps and unique request IDs so you can trace a specific run through all stages.

Patterns for Stage Coordination

Sequential: Stages run one after another, each receiving the previous stage’s output. Simple, predictable, easy to debug. Use this as the default.

Parallel fan-out: Multiple stages run simultaneously and their results are merged. Use this when stages are independent — don’t make Stage 2 wait for Stage 3 if they don’t depend on each other.

async def parallel_research(topics: list[str]) -> list[str]:
    tasks = [asyncio.create_task(research_async(topic)) for topic in topics]
    return await asyncio.gather(*tasks)

Conditional branching: Different stages run based on the output of earlier stages. For example, a triage stage might route tasks to different specialists based on content type.

Common Mistakes

Over-engineering: Start with a sequential pipeline and add parallelism or branching only when you have a concrete reason. Parallel stages are harder to debug and may not improve end-to-end latency if you have a bottleneck elsewhere.

Too many LLM stages: Every stage that uses an LLM adds latency, cost, and non-determinism. If two stages could be combined into one prompt, consider combining them.

No error handling: What happens if an LLM call fails? If an external API times out? Build retry logic into each stage from the beginning.

When to Use Agentic Workflows vs Single Agents

An agentic workflow is more work to build than a single agent. It’s worth the investment when:

The task has natural phases with different requirements: Research requires search tools and patience. Analysis requires reasoning and possibly code execution. Writing requires a different tone and focus. Splitting these into separate pipeline stages lets you optimize each stage independently — different system prompts, different models, different tool sets.

The pipeline will run repeatedly on similar inputs: A one-off task might not justify the engineering overhead of a multi-stage pipeline. But if you’re running the same research-analyze-write workflow daily, the investment in a well-structured pipeline pays off quickly.

You need to inspect intermediate outputs: If a stakeholder needs to review the research before analysis proceeds, or a human needs to approve the analysis before writing begins, a pipeline structure makes those checkpoints natural.

Reliability matters: Single agents fail silently in ways that are hard to attribute to a specific cause. Pipelines fail at a specific stage with a specific input, which makes failures much easier to diagnose and fix.

For truly one-off, exploratory tasks where the structure is unclear in advance, a single flexible agent is often easier to get started with. You can always refactor into a pipeline once you understand the shape of the work.