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How Do I Design Evals for an Agent That Takes Multiple Steps?

In the fast-evolving world of AI-powered agents, product managers and AI leads face a nuanced challenge: how to effectively evaluate a complex multi-step agent before shipping. Whether you’re building intelligent automation for finance at PayPal, leveraging advanced ML tools like those at H2O.ai, or managing product strategy with frameworks from PM Toolkit, the principles of designing meaningful evals remain critical. This guide demystifies agent eval design and offers a step-by-step blueprint for crafting rigorous multi-step agent evaluations.

Why the Model Is Not the Product

First and foremost, it’s imperative to remember—no matter how impressive your AI model is, it’s not the entire product. The product includes user experience, integration points, workflows, and crucially, the business context. From my 12 years in B2B SaaS and years shipping AI-assisted workflows, I’ve learned that teams focusing solely on raw model metrics like accuracy or perplexity miss the bigger picture. Models serve a function within a system, and that function varies by task and user need.

Consider PayPal’s intelligent fraud detection system. The underlying model identifies suspicious transactions, but the product integrates these signals with user alerts, human oversight workflows, and risk scoring policies. The evaluation strategy must reflect how each step contributes to the ultimate goal: reducing fraud losses with minimal friction for users.

Defining an Eval Before Release: Setting the Foundation

Before you even run the first test, define what success looks like and what you are measuring. Without a clearly articulated evaluation plan, you’re flying blind.

  • Clarify the task: What specific problem is the agent solving, and what are its expected outputs at every step?
  • Identify user workflows: Observe what users do today and how your agent plugs in or changes that workflow.
  • Determine key metrics: Use frameworks like PM Toolkit calculators to align evaluations with business goals—For example, translating agent accuracy to impact on TAM, ROI, retention, churn, or NPS scores.
  • Decide scope: Will your eval focus on end-to-end success or granular step-level precision? Both are important but inform different insights.

Here's what kills me: tools like pm toolkit’s tam/sam/som, roi, business case, https://smoothdecorator.com/what-does-the-model-is-not-the-product-mean-for-product-managers/ and rice calculators help prioritize what to measure. For instance, a high expected ROI may justify investing more eval resources in complex step-level analyses.

Understanding Task-Dependent Model Behavior and Failure Modes

Multi-step agents behave differently depending on task complexity and domain-specific challenges. Step-level failures can cascade and degrade the whole system’s utility. Here’s how to approach this:

1. Decompose the Agent’s Workflow

Break down the agent’s operation into discrete steps or sub-tasks. For example, an AI assistant might:

  1. Parse user intent
  2. Retrieve relevant documents
  3. Generate a draft response
  4. Summarize action items
  5. Trigger tool calls (e.g., calendars, email clients)

Each of these steps can fail independently or interactively, so you need specific evals at each level.

2. Identify Common Failure Modes at Each Step

Step Potential Failure Mode Impact Intent Parsing Misclassification or ambiguous interpretation Wrong downstream actions or no action Document Retrieval Irrelevant or incomplete results Inaccurate or misleading responses Response Generation Hallucinations or factual errors User mistrust and reduced engagement Summarization Omission of key points or verbosity Lower actionability Tool Calling Incorrect API calls or failed commands Broken automation and user frustration

Spotting these failure modes requires granular step-level evaluations—what I call step level evals.

3. Design Tailored Eval Metrics for Each Step

  • Intent Parsing: Precision, recall, confusion matrices on labeled datasets.
  • Document Retrieval: Relevance scores, precision@k, recall@k.
  • Response Generation: Factual accuracy, hallucination rates (careful not to treat benchmark hallucination rates as product guarantees!), fluency, and user satisfaction.
  • Summarization: Coverage, compression ratio, ROUGE scores, and manual review.
  • Tool Calling: Success/failure rates of API invocations and latency metrics.

At PayPal, for example, evaluating intermediate outputs like flagged items before final fraud confirmation was essential for reducing false positives and maintaining user trust.

Automation vs Augmentation vs Innovation Patterns

Understanding how your multi-step agent fits into these three product patterns shapes eval strategy as well:

  • Automation: The agent replaces manual human effort—e.g., auto-processing invoices. Evals should emphasize error rates and time saved vs human baseline.
  • Augmentation: The agent assists humans in decision making—e.g., suggesting next best actions. Evals prioritize correctness, explainability, and user satisfaction (NPS, PMF score).
  • Innovation: The agent enables new workflows or products—e.g., entirely new customer service channels using AI. Here, eval focuses more on adoption rates (DAU/MAU), activation rate, and business impact (MRR/ARR).

These patterns help prioritize which step-level failures are showstoppers versus acceptable tradeoffs. For example, an augmentation agent may tolerate slight errors if it improves workflow velocity or cycle time, while an automation agent demands near-perfect operations.

Practical Tools for Multi-Step Agent Eval Design

In the initial AI product stages, lightweight tooling enables rapid iteration:

  • Spreadsheets for Evals: Build customized eval sheets to track step success/failure frequencies, annotate failure modes, and correlate with business KPIs. Spreadsheets offer flexibility unmatched by rigid platforms.
  • PM Toolkit Calculators: Use calculators like RICE, ICE, Kano, Impact/Effort, and especially sample size to design statistically grounded A/B tests that validate agent impact.
  • Business Case Models: Translate step-level performance improvements to business metrics using ROI, MRR/ARR, LTV/CAC, and retention models—helping justify investments in model improvements or workflow redesigns.

PM Toolkit’s calculators and frameworks help avoid hand-wavy “accuracy improved” claims and instead anchor evals in tangible business outcomes, a lesson every AI PM should internalize.

Case Study: Designing Evals for a Multi-Step Agent at H2O.ai

At H2O.ai, where AI models are integrated into enterprise ML workflows, product teams design agents that assist data scientists in automated model tuning and deployment:

  1. Step Decomposition: The agent suggests model features, runs hyperparameter tuning, validates model performance, and finally triggers deployment.
  2. Step-Level Eval: Using test datasets and synthetic examples, each step is assessed for correctness, runtime efficiency, and failure modes.
  3. Business Metrics Mapping: Using PM Toolkit ROI and business case calculators, teams measure how agent automation accelerates cycle time and improves production model success rates.
  4. Fail-Safes: Built-in kill switches allow manual override if any step shows anomalous behavior—crucial for a safe release strategy.

This multi-dimensional Click for more eval framework empowers the team to confidently ship complex agents that augment data scientists rather than replace them, maximizing impact with minimized risk.

Summary Checklist for Multi-Step Agent Eval Design

Phase Checklist Item Planning Define agent task and expected outputs per step Planning Map user workflows and existing behavior Metrics Identify relevant step-level metrics (accuracy, latency, error rates) Metrics Link metrics to business outcomes using PM Toolkit calculators (ROI, churn, NPS) Design Build granular tests and datasets for each step Design Distinguish failure modes with annotations and qualitative review Testing Run step-level evals before full end-to-end testing Testing Design A/B tests with sample sizes and power calculators Release Implement kill switches and monitoring dashboards

Closing Thoughts

Designing evals for agents that take multiple steps demands a product-minded approach—rooted in understanding “what users do today,” clearly defining success metrics, and translating model outputs into business outcomes. Avoid falling into “model wars” focused on isolated benchmarks without workflow context. By leveraging lightweight, iterative eval frameworks in spreadsheets combined with PM Toolkit’s calculators and frameworks, you can create robust, transparent, and actionable evaluations that de-risk shipping and maximize value.

Remember, the model is a means to an end, not the end itself. Your AI agent’s multi-step complexity is an opportunity to design elegant augmentation or automation—not just chase abstract accuracy gains. Follow this guide to bring clarity and rigor to your agent eval design and build AI products your users trust and love.