Evaluating Agentic Workflows: How to Reliably Test Multi-Step AI Systems

The era of AI agents is here. These sophisticated, multi-step AI systems promise to revolutionize how we work by automating complex tasks, from customer support and data analysis to software development itself. But as we build these powerful agents, a critical question emerges: How do you know if they're actually working well? More importantly, how do you verify that a change you made actually improved performance?

For developers building these "Services-as-Software," traditional testing methods are proving inadequate. You can't just write a unit test for an LLM's creativity or a RAG pipeline's relevance. Relying on a handful of manual "vibe checks" is slow, subjective, and simply doesn't scale. To build robust and reliable agentic workflows, we need to move from guesswork to data-driven evaluation.

Why Testing Agentic Workflows is a Herculean Task

Evaluating a multi-step AI system is fundamentally different from testing traditional code. The challenges are unique and require a new way of thinking.

• The Non-Determinism Dilemma: Large Language Models (LLMs) are inherently non-deterministic. The same prompt can yield slightly different outputs on subsequent runs. This makes establishing a stable baseline for comparison incredibly difficult.
• Cascading Errors: An agentic workflow is a chain of actions. A small error in an early step—a misunderstood instruction, a failed tool call, or a suboptimal RAG retrieval—can snowball, leading to a completely incorrect final output. Pinpointing the root cause is a nightmare.
• Balancing Conflicting Metrics: What defines a "better" agent? Is it the one with the highest quality output, the lowest latency, or the cheapest operational cost? Often, these goals are in direct conflict. Optimizing for one can easily degrade another, making LLM Evaluation a complex balancing act.
• The Flaw of Ad-Hoc Prompt Engineering: Many teams are stuck in a cycle of tweaking a prompt, running it once or twice, and deciding "it feels better." This approach is prone to bias and lacks statistical rigor. A prompt that works for one query might fail spectacularly on the next ten.

Without a systematic approach, you're flying blind, unable to prove whether your changes are genuine improvements or just random fluctuations.

From Vibe Checks to Victory: The Power of Systematic AI Experimentation

The solution is to adopt the same scientific rigor that powers growth marketing and product development: systematic A/B testing. By applying this experimental mindset to AI development, you can move beyond subjective feelings and make decisions based on cold, hard data.

Effective AI Experimentation hinges on three core principles:

1. Define Variants: Isolate the component you want to improve. This could be a single prompt, a new LLM, a different set of retrieval documents for RAG Optimization, or even an entire end-to-end agentic workflow.
2. Establish Metrics: Define what success looks like before you start. This involves choosing objective, measurable KPIs like cost, latency, response quality, factual accuracy, or any custom business metric.
3. Run and Analyze: Execute both variants against a representative set of inputs and collect performance data. Use statistical analysis to determine if there is a significant difference and declare a winner.

This is precisely the problem we built Experiments.do to solve. It’s an agentic testing platform designed for developers who need to iterate on prompts, models, and full RAG pipelines to find the optimal configuration for any AI-powered workflow.

A/B Test Your AI: A Practical Guide with Experiments.do

Experiments.do provides an API-first platform to embed continuous, data-driven improvement directly into your development lifecycle. Instead of manual checks, you can programmatically define, run, and analyze experiments.

Imagine you're building a customer support agent. A critical step is composing the final response to a user's query. You're unsure whether a direct, concise tone is more effective than a more empathetic one. With our SDK, defining this A/B testing AI experiment is trivial:

import { Experiment } from 'experiments.do';

// Define an experiment to compare two different LLM prompts
const exp = new Experiment({
  name: 'Support-Response-Prompt-V2',
  variants: {
    'concise': { prompt: 'Solve the user issue directly.' },
    'empathetic': { prompt: 'Acknowledge the user\'s frustration, then solve.' }
  }
});

// Run the experiment with a sample user query
const results = await exp.run({
  query: 'My login is not working',
  metrics: ['cost', 'latency', 'customer_sentiment']
});

console.log(results.winner);

In this example, we create an experiment with two variants: concise and empathetic. When we run it, Experiments.do will execute both prompt strategies, collecting data on cost, latency, and a custom customer_sentiment metric. The platform then handles the statistical analysis to tell you which variant is the winner, removing all the guesswork from your Prompt Engineering process.

Putting it all Together: Testing a Full Agentic Workflow

The true power of this approach shines when evaluating complex, multi-step agents.

Let's consider a research agent designed to answer complex questions. The workflow involves:

1. Decomposition: Breaking the main question into sub-queries.
2. Tool Use (RAG): Searching a knowledge base for relevant information for each sub-query.
3. Synthesis: Compiling the retrieved information into a coherent answer.

You want to test two completely different configurations:

• Workflow A ("The Sprinter"): Uses a faster, cheaper model like Claude 3 Haiku for all steps and a basic vector search for RAG.
• Workflow B ("The Scholar"): Uses a powerful model like GPT-4o for synthesis, a dedicated routing agent to choose the right tools, and an advanced RAG pipeline with reranking for higher relevance.

With Experiments.do, you can define these two entire chains as variants. You would then run a batch of 100 diverse research questions through both workflows. Our platform would automatically track your predefined metrics for each run, such as:

• end_to_end_latency
• total_cost
• factual_accuracy (evaluated by an LLM-as-judge or human)
• answer_completeness

After running the experiment, you would get a clear, data-backed report showing that while "The Scholar" produces 15% more accurate answers, "The Sprinter" is 80% cheaper and 3x faster. This allows you to make an informed trade-off based on your product's specific needs, rather than an uninformed guess.

Build Better AI, Faster

Agentic workflows hold immense potential, but they also introduce a new level of complexity. To build reliable, high-performing, and cost-effective AI agents, you must move beyond ad-hoc testing. A disciplined, data-driven approach to AI Experimentation is no longer a luxury—it's a necessity.

By treating every change as an experiment and every component as testable, you can systematically optimize your AI systems. Experiments.do provides the purpose-built infrastructure for this new paradigm of AI development, empowering you to A/B test everything from a single prompt to an entire agent.

Ready to stop guessing and start optimizing? Explore Experiments.do and run your first data-driven AI experiment today.