Boost AI Agent Performance Through Evaluations and Error Analysis (Part 1)

Discover how to systematically evaluate and refine AI agents using outcome-based evals and error analysis techniques. This guide provides actionable steps with examples to improve agent reliability in complex tasks.

The Rise of Agentic AI and the Need for Robust Evaluations

Agentic workflows represent a significant evolution in AI applications, where large language models (LLMs) don't just generate responses but actively pursue goals through planning, tool usage, and iterative decision-making. These systems excel at handling multi-step tasks like booking travel or debugging code, yet their complexity introduces unique failure modes. Traditional LLM evals, focused on single outputs, fall short here. Instead, specialized evaluations are essential to measure end-to-end performance and uncover issues.

In this first part, we explore outcome supervision—evaluating final results—and lay the groundwork for error analysis. Subsequent parts will cover process supervision and advanced debugging. By implementing these practices, developers can iteratively enhance agent reliability, turning unreliable prototypes into production-ready tools.

Key Reasons to Prioritize Agent Evaluations

Evaluating AI agents goes beyond basic accuracy checks. Here's why it's critical:

• Complexity Amplifies Errors: Agents chain multiple LLM calls, tools, and loops, creating cascading failures. A small planning error can derail the entire process.
• Non-Deterministic Behavior: Temperature settings and model variability lead to inconsistent outcomes, necessitating large test suites.
• Real-World Stakes: In applications like customer support or financial analysis, poor performance risks tangible harm.
• Optimization Demands Data: Evals provide quantitative metrics to compare models, prompts, or architectures.

Consider a travel booking agent: it must research flights, hotels, and itineraries while respecting user constraints. Without evals, you'd rely on manual testing, which is unscalable.

Outcome Supervision vs. Process Supervision: Choosing the Right Approach

Two primary eval paradigms exist for agents:

Outcome Supervision

Focuses on the final result. Simple and scalable, it checks if the agent achieved the goal (e.g., "Did it book a valid flight?") without inspecting intermediate steps.

• Pros: Easy to implement; automatable with rule-based graders.
• Cons: Misses why failures occur; opaque to internal reasoning flaws.

Process Supervision

Examines intermediate steps, like plan quality or tool usage correctness.

• Pros: Pinpoints exact failure points for targeted fixes.
• Cons: Requires more annotation effort; harder to scale.

For starters, begin with outcome evals, then layer in process checks as agents mature. This hybrid approach mirrors software testing: unit tests (process) complement integration tests (outcome).

Building and Evaluating a Sample Travel Agent

To illustrate, let's use a practical example: a Pydantic-powered travel agent. This agent plans trips by querying mock APIs for flights and hotels, then generates itineraries. You can access the full implementation here.

Step 1: Set Up Your Environment

Use LangSmith, a platform for LLM tracing and evals (free tier available). Install dependencies:

pip install langsmith pydantic-ai-agent

Set your API keys:

export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=your_key
export LANGCHAIN_PROJECT=travel-agent-evals  # Optional: for organizing runs

Step 2: Define Test Cases

Create a dataset of inputs and expected outcomes. For outcome evals, specify ground-truth results. Example test cases in JSONL format:

{"input": "Book a flight from SFO to JFK on 2024-10-15", "output": "success"}
{"input": "Find hotels in Paris for 3 nights starting 2024-11-01 under $200/night", "output": "success"}
{"input": "Plan a trip to Mars", "output": "failure"}  // Edge case

Upload to LangSmith:

from langsmith import Client

client = Client()
dataset_name = "travel-agent-test-suite"

client.create_dataset(dataset_name=dataset_name)
# Add examples via UI or API

Aim for 50-100 diverse cases covering happy paths, errors, and constraints.

Step 3: Run Outcome Evaluations

LangSmith automates agent runs against your dataset. Define a simple grader function:

def outcome_grader(run, example):
    # Check if final output matches expected
    agent_output = run.outputs['output']
    expected = example.outputs['output']
    return "correct" if agent_output == expected else "incorrect"

# In LangSmith UI: Create eval with this grader

Key metrics emerge:

• Pass Rate: % of successful outcomes.
• Latency: End-to-end time.
• Cost: Token usage.

Visualize traces to see where agents loop excessively or hallucinate.

Conducting Error Analysis: From Metrics to Insights

Raw pass rates are a starting point; error analysis reveals root causes. Follow this methodical process:

1. Filter Failures: In LangSmith, query low-performing runs (e.g., pass_rate < 0.7).
2. Inspect Traces: Review step-by-step execution. Look for:
   • Poor planning (e.g., ignoring budget).
   • Tool misuse (e.g., wrong API params).
   • Infinite loops.
3. Categorize Errors: Use tags like "planning_failure", "tool_error", "hallucination".
4. Prioritize Fixes: Address high-frequency issues first.
   • Example: If 40% fail on date parsing, refine the prompt with few-shot examples.
5. A/B Test Iterations: Re-run evals post-changes to measure uplift.

Real-World Example: Debugging the Travel Agent

Testing revealed common pitfalls:

• Ambiguous Queries: "Cheap flight to Europe" → Agent picks wrong origin. Fix: Add clarification tools or user confirmation loops.
• API Mock Failures: Simulated errors not handled gracefully. Fix: Implement retry logic with exponential backoff.

After tweaks, pass rate jumped from 62% to 89%.

Best Practices for Scalable Agent Evals

• Version Everything: Tag datasets and agent versions for reproducibility.
• Automate CI/CD: Integrate evals into pipelines; block deploys on <80% pass rate.
• Human-in-the-Loop: Spot-check 10% of failures for grader refinement.
• Scale with Synthetics: Generate test cases via LLMs for coverage.

Tools like LangSmith shine here, offering dashboards, annotations, and experiment tracking. For open-source alternatives, explore libraries like DeepEval or custom Pytest setups.

Looking Ahead: Process Evals and Beyond

Outcome evals validate "what" works; process evals explain "how." In Part 2, we'll dive into trajectory criticism, reward modeling, and advanced tracing. Start today with the Pydantic AI Agent repo, run your first eval suite, and watch your agents transform from brittle to robust.

This approach isn't theoretical—teams at companies like Replicate and Adept use similar methods to ship reliable agents. Apply these steps to your projects for measurable gains in performance and trust.