Agentic Testing (Agent Evaluation)

As modern applications increasingly integrate Large Language Models (LLMs) and autonomous AI agents (such as LangChain, LangGraph, custom API agents, and Model Context Protocol / MCP tools), traditional deterministic assertions are no longer sufficient. Non-deterministic model responses, multi-step tool-calling loops, and hallucinations require a new approach to quality assurance: Agentic Testing.

Robonito's Agent Evaluation Workbench provides an end-to-end testing platform to benchmark, evaluate, and regression-test autonomous AI agents across repeatable experiments.

Agent Evaluation Workbench showing dataset rows, agent trajectory, and evaluation metrics

Why Agentic Testing?

AI agents differ fundamentally from traditional software:

  • Non-Determinism: The same prompt can yield different reasoning trajectories or wording across runs.
  • Complex Tool Chains: An agent might make multiple sequential tool calls (database queries, web searches, API calls) before returning a final answer.
  • Hallucinations & Drift: Updates to system prompts, underlying model weights (e.g., upgrading from GPT-4o to Claude 3.5 Sonnet or Gemini 3 Flash), or tool schemas can silently degrade reliability.

Robonito transforms agent evaluation into a disciplined, automated QA process with durable experiment tracking, multi-lens metrics, and regression comparisons.

Core Architecture & Workflow

Agentic testing in Robonito revolves around five core concepts:

┌─────────────────────────┐      ┌─────────────────────────┐
│   Evaluation Dataset    │ ───► │  Agent Target Profile   │
│ (Prompts, Ground Truth) │      │  (HTTP / MCP / LangGraph)│
└─────────────────────────┘      └────────────┬────────────┘
                                              │ Executes N Repetitions
                                              ▼
┌─────────────────────────┐      ┌─────────────────────────┐
│  Regression Matrix &    │ ◄─── │ Multi-Lens Evaluators   │
│  Candidate Comparison   │      │ (Task, Tools, Halluc.)  │
└─────────────────────────┘      └─────────────────────────┘

1. Evaluation Datasets

An Evaluation Dataset is a versioned collection of test scenarios used to benchmark your agent. Each row in a dataset defines:

  • Input Prompt: The user message or task assigned to the agent (e.g., "Book the cheapest flight from London to Dubai on Nov 15th").
  • Context & Constraints: Required constraints, personas, or mock data slots.
  • Expected Outcome / Ground Truth: The target business outcome, expected output format, or verified facts.
  • Tool Ground Truth: The list of tools the agent is expected to invoke (and tools it must not invoke).

Datasets can be created manually in the workbench, uploaded via CSV/JSON, or converted from real production interaction logs.

2. Agent Target Profiles & Connections

Robonito connects to your AI agent using standardized target profiles:

Connection TypeProtocolHow It Connects
REST / HTTP AgentHTTP/JSONSends input payloads to your agent's API endpoint and parses streaming or batch responses.
Model Context Protocol (MCP)JSON-RPC / MCPConnects directly to MCP servers, verifying tool registrations, parameter schemas, and authentication slots.
LangGraph / LangChainPython / JS RuntimeDispatches runs directly to local or cloud-hosted LangGraph agent state machines.
OpenTelemetry (OTLP)OTLP / W3C TraceIngests distributed traces directly from your agent runtime, linking Robonito's test execution spans with the agent's internal reasoning spans.

3. Multi-Lens Evaluators

Rather than relying on a single pass/fail metric, Robonito evaluates agent runs across multiple specialized lenses:

Task Completion & Business Outcome

Validates whether the agent reached the requested terminal state. For example, in an e-commerce agent, search results or clicking an "Add to Cart" button do not substitute for a verified cart count and order confirmation.

Tool-Calling Precision & Safety

  • Tool Selection: Did the agent invoke the right tools with schema-compliant arguments?
  • Omission Testing: Verifies that agents do not invoke prohibited or out-of-scope tools.
  • Parameter Accuracy: Validates that extracted entities (dates, IDs, quantities) passed to tools match the user prompt.

Hallucination & Factuality Testing

An LLM judge compares the agent's claims against the retrieved ground truth and tool outputs:

  • Faithfulness: Verifies that every assertion made by the agent is grounded in retrieved context.
  • Answer Relevance: Ensures the response directly addresses the user's intent without superfluous filler.

Performance & Cost Governance

  • Latency & Time-to-First-Token: Measures round-trip time across each step in the agent's loop.
  • Token Budgets & Cost Receipts: Enforces hard caps on input/output tokens per attempt with durable operation receipts.

4. Running Experiments & Repetitions

In the Agent Experiment Workbench:

  1. Select your target agent connection and the test dataset.
  2. Configure the Repetition Count (e.g., 3 to 10 repetitions per row) to measure statistical stability and non-deterministic variance.
  3. Set your token budget and latency thresholds.
  4. Click Run Experiment.

Robonito executes the batch using durable, leased attempts. If your desktop app or network disconnects mid-experiment, progress is preserved and incomplete work resumes automatically.

5. Regression Matrix & Candidate Comparison

When you tweak a system prompt, change tool definitions, or test a new foundational model (e.g., comparing Candidate A vs. Candidate B):

  1. Run the evaluation dataset against both candidate configurations.
  2. Open the Candidate Comparison View:
    • Win / Loss / Tie Scoring: See immediately which candidate performs better overall.
    • Per-Metric Deltas: Compare pass rates, hallucination rates, and tool accuracy with confidence intervals.
    • Cost vs. Latency Tradeoffs: Visualize whether a more capable model justifies its higher token cost and latency.
    • Causal Evidence Links: Click on any regression to view the exact trace timeline, tool parameters, and model outputs that caused the failure.

Best Practices for Agent Quality Engineering

  • Pin Evaluation Definitions: Store agent test configurations as immutable versions so benchmarks are strictly comparable over time.
  • Never Rely on Self-Reporting: Always verify concrete business outcomes (e.g., database records created or API side-effects confirmed) rather than trusting an agent's self-generated confirmation text.
  • Include Negative Test Cases: Include adversarial prompts and out-of-domain requests in your evaluation sets to ensure guardrails hold under stress.