Day 17/90 — Compare Agent Execution with a Workflow Baseline

An AI agent can make decisions, select tools, and adapt its execution.

But does that make it better than a predefined workflow?

Consider a document support assistant:

• Find a customer's order.
• Retrieve the refund policy.
• Check whether the customer is eligible for a refund.
• Prepare a response.

A predefined workflow can execute these steps in a fixed sequence.

An agent can decide which tools to use and whether additional steps are necessary.

Both approaches may solve the same task, but their performance can differ.

I'm exploring how to compare agent execution with a workflow baseline.

What's the difference?

A workflow follows predefined execution paths. An agent dynamically decides its next actions based on the task and tool results.

Workflow	Agent
Predetermined execution path	Dynamic decision-making
More predictable execution	More flexible execution
Easier to control	Requires execution boundaries
Suitable for structured tasks	Useful for less predictable tasks

Neither approach is automatically better.

The right choice depends on task complexity and performance requirements.

What does the research suggest?

Anthropic's Building Effective Agents makes an important recommendation: start with the simplest solution and introduce agentic complexity only when necessary.

Agent systems can improve performance on complex tasks, but they can also increase latency, cost, and the risk of compounding errors.

LangGraph documentation makes a similar architectural distinction: workflows use predetermined paths, while agents dynamically determine their processes and tool usage.

The takeaway is simple:

Autonomy should solve a measurable problem, not just make an architecture more complicated.

Design a fair comparison

To evaluate both approaches, use the same set of tasks.

For example:

Test task	Expected outcome
Track an existing order	Return correct order information
Explain the refund policy	Retrieve the relevant policy
Check refund eligibility	Apply the correct business rules
Handle missing order details	Ask for clarification
Handle an unsupported request	Use the fallback

Keep the following conditions consistent wherever possible:

• Same underlying model.
• Same tool implementations.
• Same input dataset.
• Same business rules.
• Same success criteria.

The main variable should be the execution architecture.

Measure more than task completion

A system that completes tasks successfully but takes significantly longer or costs more may not be the better choice.

I would compare these metrics:

Metric	What it measures
Success rate	Percentage of tasks completed correctly
Latency	Time required to complete a task
Cost per task	Model and tool execution cost
Tool calls	Number of tool invocations
Failure rate	Tasks that fail or produce incorrect results

Run multiple trials for tasks where model behavior varies, and report the number of runs alongside the results.

Example evaluation report

The following is an illustrative reporting format, not measured project data.

Metric	Workflow	Agent
Successful tasks	—	—
Average latency	—	—
Average cost	—	—
Average tool calls	—	—

Once both implementations are tested, these values can help identify whether agent autonomy provides enough improvement to justify its additional overhead.

My engineering takeaway

I wouldn't introduce an agent simply because a task involves multiple tools.

If the execution path is predictable, a workflow may be sufficient.

If the task requires dynamic decisions, recovery, or choosing different tools based on intermediate results, an agent may be worth evaluating.

The goal isn't to make everything autonomous. It's to choose the simplest architecture that meets the task's requirements.

When building an AI application, do you establish a workflow baseline before introducing an autonomous agent?

References

• Anthropic (2024). Building Effective Agents.
• LangChain. Workflows and Agents — LangGraph.
• Anthropic. Demystifying Evals for AI Agents.