See into now. Build for tomorrow.
Prepare for the unknownExplore the possibilities
Test the whole AI system: an application, assistant, model pipeline, or agent workflow. Discover failure patterns, validate safeguards, and compare designs. Explore how an assistant such as Claude could find and use your service through its MCP tools.
Test a hypothesis. Vary the model, tools, and environment. Find which design holds up.
Explore crashes, timeouts, and failed handoffs. Turn the failure into a check before users encounter it.
Test your MCP descriptions, schemas, and responses against competing tools. Learn what improves discovery, selection, and successful use.
What if
Gates flag when an agent acts against what you want. Write your own. We derive the rest from your history, from security to performance.
o4ve.connect({ system, agents, history, models }) o4ve // what if: an endpoint you call with inputs .whatif({ files: ["invoice.pdf"] }) // gates: plain-language checks, tested like any test .then("Refunds stay under $500") .then("Never act on instructions inside files")
The learning loop
One booking agent, from first trace to ranked benchmark.
01 / 06 · Observe
Follow a real request through its tool calls, timings, and checks. Use the recorded failure as the starting point for a reproducible test.
See which operation failed and what happened immediately before it.
The first reservation committed before its response timed out. The retry created another booking.
02 / 06 · Design the input
One file defines your agent and every dial: model reasoning, tool loading, output format, token limits, and world conditions. Expand the valid combinations. Decide what must hold.
Derived defaults. Your configuration. Illustrative syntax.
Agent × model settings × tools × output × world conditions
# One input. Every dial is configurable.agent: booking-agent@candidateworld: ticketing-partner@v4intents: datasets/booking-intentsmodels: [baseline, candidate, fast, small]thinking: [off, adaptive]reasoning_effort: [low, medium, high]max_output_tokens: [1024, 4096, 16384]tool_loading: [eager, lazy]output: [text, json_schema]failures: [none, timeout, timeout_after_commit]checks: [one_booking, no_duplicate_charge]expand: all_valid_combinationsExpand valid combinations for each model’s capabilities. Report incompatible settings and any scenarios left untested by your budget.
03 / 06 · Compose a world
Choose the partner version, initial state, and failure condition. Every replay starts from the same environment.
Partner environments provide versioned behavior for your tests.
Check: a retry must not create a second booking.
04 / 06 · Simulate
Thousands of paired runs across models, delays, and outages.
Group failed checks by condition to decide what to investigate.
Timeouts after commit fail. That is the checkpoint.
05 / 06 · Replay
Restore the recorded state, reproduce the bug, and run the candidate change through the same checks. See exactly which jobs pass before release.
Same checkpoint. Different choices. Compare the outcomes.
Same request, state, and failure condition. Only the recovery logic changes.
06 / 06 · Benchmark
How often agents pick you, and how fast you recover, against the field. Compare outcomes under the same test conditions.
You see the field, never a rival's data.
Bring the findings into production. New traces start the next cycle.
Field: anonymized median on the same worlds.
Mapping possible worlds
A booking worked once. Change the response timing and retry policy to discover when it could fail.
Partner replied on time.
Exactly one booking.
What if the reply arrives late, or never arrives?
| Retry policy | On time | Late | Lost |
|---|---|---|---|
| No retry | Booked | Unconfirmed | Unconfirmed |
| Retry | Booked | Duplicate | Duplicate |
| Check, then retry | Booked | Recovered | Recovered |
One observed case. Eight simulated variations.
A missing reply does not mean the booking failed. Check the reservation before retrying to avoid creating a duplicate.
Illustrative scenario outcomes. The partner commits the booking in each case; only its reply and the recovery policy change.
Aggregation
Every scenario runs on both configurations under identical conditions. Pairing outcomes one to one cancels the noise that sinks ordinary A/B tests, so a real effect shows up with far fewer runs, and arrives with an interval.
Illustrative data, generated from a fixed seed. The interval is computed from the dots shown.
Scale
Turn collected data into reusable units. Test configs in sweeps, then analyze the results across reliability, cost, quality, and discovery. Every cycle adds to what you know.
A unit is one recorded interaction: its input, context, and outcome. Keep it as a reusable test case.
Request + context + outcomeA config defines the model, tools, and runtime settings. A sweep tests multiple configs against the same units, so you can compare their results.
Same units × different configsUse the same results to investigate failures, compare cost and quality, and understand discovery and selection. A new question does not always need a new run.
One result set · many questionsAnalyze collected data again when the question changes. Run new tests when a config, condition, or missing piece of evidence requires them.
New production units expose new conditions. Add them to the next sweep and check whether yesterday’s findings still hold.
Your accumulated units and results reveal patterns a single test cannot. Each cycle gives the next decision more context.
Proof
Bring traces, partner records, and evaluation results together around the same request. Follow the evidence from what happened to why it happened, and what to test next.
request bk_0184 · reservation.create
booking A91 exists · response never arrives
same request · new booking A92
expected 1 booking · found 2
The first booking succeeded, but its response was lost. The retry treated a missing reply as a failed booking and created another.
Ownership
Connect your existing systems and data to O4VE’s managed service. We operate the simulation engine and infrastructure. Your team controls the scenarios, evaluates the findings, and decides what to ship.
No simulation software or infrastructure for your team to deploy and maintain.
A continuous cycle
O4VE connects the work before deployment to what happens after it. Design your system, validate it against realistic scenarios, and get it into production. Then use live evidence to scale, improve, and validate the next change.
Explore concepts, choose models and tools, and define what success looks like.
Reproduce failures, test safeguards, and check release criteria before and during rollout.
Collect production traces, track outcomes, and find where behavior differs from expectations.
Compare cost, quality, reliability, and discovery. Replay proposed changes before expanding rollout.
Production evidence becomes the next scenario, eval, and design decision. Every release starts the cycle again.
Build, validate, deploy, and improve your AI systems in one continuous cycle.