Agent Testing and Evaluation Using Autonomous Vehicle Simulation Principles

coval · Tech · 2023 · source Agentic: yes · promotional bias: med · source-backed · extracted with prompt 0.5.2 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Agent evaluation

Coval needed scalable testing for conversational agents beyond slow manual vibe checks.

Constraints:

Architecture

Coval's evaluation architecture applies self-driving simulation ideas to conversational and autonomous agents. Instead of engineers manually vibe-checking individual paths, Coval defines high-level natural-language scenarios from workflows, RAG documents, or other agent context; generates many test cases and pathways; simulates conversations or agent interactions; scores aggregate success with LLM judges, heuristics, and metrics; and encourages redundancy and fallback design so failures degrade gracefully rather than breaking the whole agent experience.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  workflow-and-context-inputs[Workflow and context inputs] --> scenario-generator[Scenario generator]
  scenario-generator --> scenario-simulator[Scenario simulator]
  agent-under-test[Agent under test] --> scenario-simulator
  scenario-simulator --> layer-simulator[Layer simulator]
  scenario-simulator --> llm-judge-and-heuristics[LLM judge and heuristics]
  llm-judge-and-heuristics --> aggregate-metrics[Aggregate metrics]
  aggregate-metrics --> fallback-design-loop[Fallback design loop]
  fallback-design-loop --> agent-under-test
Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component Role Interacts with
Agent under test Conversational, voice, web, or autonomous agent being evaluated. Scenario simulator
Workflow and context inputs Documents, workflows, or high-level natural-language objectives that inform scenarios. Scenario generator
Scenario generator Creates many test cases from high-level agent goals and context. Workflow and context inputs, Scenario simulator
Scenario simulator Runs repeated simulated pathways against the agent and selected system layers. Scenario generator, Agent under test, Layer simulator
Layer simulator Simulates only relevant layers such as conversation, voice, or web behavior. Scenario simulator
LLM judge and heuristics Scores goal achievement and behavior across simulations. Scenario simulator, Aggregate metrics
Aggregate metrics Reports success, failure, and event rates across many generated tests. LLM judge and heuristics, Fallback design loop
Fallback design loop Uses failures to design redundancy, fallback, and graceful degradation. Aggregate metrics, Agent under test

Data flow

An agent team supplies workflows, documents, or high-level scenarios. Coval generates scenario variants, runs them repeatedly against the agent, simulates only the necessary system layers, scores outcomes with judges or heuristics, reports aggregate metrics across many runs, and uses failures to guide fallback mechanisms or agent changes.

3 supporting quotes
“we can generate scenarios for a lot of different scenarios given certain context so if you give us workflows or if you give us reg um documents or other pieces of kind of content that inform your agent we can generate test cases from that but I think another useful”transcript, stated
“run those same scenarios maybe a hundred times and see different Pathways for that high level objective but on top of that it's also much easier to set up for say creating 50 test cases in the same way that you would write unit tests for your application like I”transcript, stated
“different iterations of booking an appointment and then saying how often was the goal achieved using LM as a judge or heuristics Etc um and this will allow us to be able to show both from a non-deterministic perspective like it's working 90% of the time um but also creating these much more”transcript, stated

Why this architecture

Coval uses probabilistic simulation because manual tests and rigid static cases are expensive to maintain and miss the combinatorial behavior of agents. Self-driving-style aggregate metrics let teams ask how often the goal is achieved across many generated paths, while layer simulation and fallback design focus effort on the parts of the system that matter.

3 supporting quotes
“testing agents is really slow in manual so today Engineers will spend hours manually testing their system and this takes a really long time because you not only have to you have to do it manually but you have to go through multiple steps in order to test that agent”transcript, stated
“self-driving then shifted I think while while I was working in self-driving towards this much larger probabilistic evaluation”transcript, stated
“you don't have to maintain each of these tests each of these individual tests but you can instead say like how often are all these things happening across all of these cases so I think we should be doing the same thing with agent I think right now people are too”transcript, stated

Models used: LLM judges

Scale

Alternatives considered (roads not taken)

Manual vibe checks after each agent change

Rejected because: Manual conversation testing takes hours and becomes whack-a-mole as changes interact.

“every time you make a change to the agent then you Vibe check chat with the system for hours find a variety of ad hoc cases that you think need Improvement and then you make another change to improve those things and this you end up playing wacka because each time”transcript, stated

Maintain rigid static tests only

Rejected because: Self-driving shifted toward generated probabilistic evaluation because static cases are expensive to maintain.

“being very rigid in those test cases and then maybe manually creating these and then running you know several uh several hundred of these or whatever sever tens anywhere from tens to thousands um but these are really expensive to maintain you have to manually go”transcript, stated

Optimize the first prompt until it never fails

Rejected because: Coval argues agents need fallback mechanisms and graceful failure because models and systems are non-deterministic.

“how do I create fallback mechanisms such that my agent can fail gracefully there's redundancy I can find the right result by you know calling multiple models Etc and this is really like once we start to think about layers of redundancy that can help really dramatically”transcript, stated

Patterns

eval-driven-development (agent-testing)

Agent changes are tested through generated scenarios instead of only manual checks.

“The problem addressed is that testing conversational AI agents is slow and manual, with engineers spending hours on repetitive "vibe checks" that become a game of whack-a-mole as changes compound.”summary, stated

llm-as-judge (agent-testing)

Coval uses LLM judges for aggregate agent-evaluation metrics.

“ith thousands of dynamically generated scenarios, simulating different system layers independently, using LLM-as-judge for aggregate metrics, and building in redundancy and fallback mechanisms. The approach enables testing at scale while reducing engineering time and improving reliability through sy”summary, stated

Tradeoffs

How to evaluate agent reliability.

Chose generated probabilistic simulation with aggregate metrics over hand-maintained static cases and manual vibe checks.

Rationale: Agent behavior is non-deterministic and combinatorial, so repeated generated scenarios reveal reliability better than a few fixed examples.

“how often was the goal achieved”transcript, stated

Gotchas (what bit them)

Manual vibe checks do not scale with agent complexity.

Root cause: Each change creates new interaction combinations.

Resolution: Coval generates many simulated scenarios.

“The problem addressed is that testing conversational AI agents is slow and manual, with engineers spending hours on repetitive "vibe checks" that become a game of whack-a-mole as changes compound.”summary, stated

Evaluation

The product itself is an evaluation system using simulation and aggregate metrics.

Metrics: scenario coverage · LLM judge scores · fallback behavior

“compound. Coval's solution draws on self-driving paradigms: moving from rigid static test cases to probabilistic evaluation with thousands of dynamically generated scenarios, simulating different system layers independently, using LLM-as-judge for aggregate metrics, and building in redundancy and f”summary, stated

Outcomes

No specific numeric production metric is provided in the summary.

“The approach enables testing at scale while reducing engineering time and improving reliability through systematic simulation rather than manual testing.”summary, stated