See which aspects of authority, data, autonomy, exposure, and scale drive the release profile.
Enterprise agent release assurance
Know what your AI agent will do before you let it act.
Connect a verified public agent or bring its full system manifest. EASAP challenges the real boundary, records what happens, and turns observed behavior into reviewable release evidence.
Testthe whole
agent system
agent system
Provefailures with
exact replay
exact replay
Deciderelease with
signed evidence
signed evidence
CONTROLLED CAMPAIGN RUNNING
AGENTCandidate
v2.4.1
v2.4.1
01Worldstate pinned
02Adversaryfault injected
03Policyboundary active
04Evidencetrace sealed
RELEASE POSTURECONDITIONAL3 controls required
deterministic replay
trace_7f2a91cdVERIFIABLEOpen reference platform
No proprietary host binding
No proprietary host binding
Exact replay
Fail-closed effects
Statistical uncertainty
Signed evidence
Use it immediately
Leave with a release plan your teams can use today.
Describe the agent’s authority once. Get its risk tier, open controls, minimum scenarios, accountable owners, approvals, and machine-readable release gate—without an account or integration.
Turn missing safeguards into concrete work for engineering, security, governance, and release teams.
Start from the failures and adversarial conditions the exact agent must survive before release.
Export required approvals, blockers, evidence retention, minimum seeds, and decision conditions.
Why you should care
An agent does not just answer. It acts.
Once an AI system can call tools, access private data, change records, move money, or represent your company, a good demo is no longer evidence of safe operation.
Typical release processEASAP assurance
Model score in isolationWhole-system behavior
Happy-path demonstrationFaults + adversaries + edge cases
Screenshot of a failureDeterministic replay
Subjective launch debateExplicit release posture
Evidence scattered across toolsSealed, portable evidence chain
How it works
From candidate to release decision in one evidence chain.
EASAP evaluates the deployed system boundary and preserves enough context to explain, replay, and govern every material result.
01
Bind the exact candidate
Pin the model, prompts, tools, memory, policies, and dependencies to immutable digests. The decision applies to one exact system—not a vague version label.
OUTPUTCandidate manifest02
Run controlled worlds
Exercise normal, degraded, and adversarial conditions with seeded state, injected faults, real policy boundaries, and mediated tool effects.
OUTPUTReproducible trials03
Measure what matters
Independent oracles score safety, policy compliance, task success, recovery, and tail risk—with uncertainty made visible instead of averaged away.
OUTPUTAssurance findings04
Issue a release posture
Convert evidence into APPROVED, CONDITIONAL, or REJECTED, with deployment conditions, expiry, revocation triggers, and a portable evidence bundle.
OUTPUTDecision + evidenceCandidate boundaryControlled executionRelease authority
What value you get
Move from “we tested it” to “we can prove why it should ship.”
The product is not another leaderboard. It is shared infrastructure for engineering velocity, operational safety, and defensible governance.
See the evidence produced by a live run ↗Faster release review
Give security, risk, and product one decision artifact instead of weeks of screenshots, spreadsheets, and competing interpretations.
Lower incident exposure
Discover unsafe tool use, privilege drift, data leakage, and brittle recovery before the agent reaches customers or production systems.
Defensible governance
Show what was tested, against which policy, with which candidate, and why a release decision was made.
Continuous regression control
Re-run the same evidence-backed gate whenever a model, prompt, tool, policy, or dependency changes.
Live product proof
Run the assurance chain yourself.
This is not a scripted animation. The public runner invokes the deterministic campaign, scoring, evidence, signing, and release-decision code covered by the repository’s tests.
01
Sealing subject, world, scenario, and seed manifests
02
Mediating tools, time, randomness, and external effects
03
Injecting stale data, permission loss, and prompt attacks
04
Evaluating oracles and estimating uncertainty
05
Signing evidence and evaluating the release gate
Procurement authority boundary
A real agent harness will process synthetic purchasing events while faults attempt to bypass policy.
STANDARD isolation · synthetic data · egress deniedWhat actually runs
Immutable manifests → seeded trials → mediated effects → causal traces → statistical measures → signed decision
Audit the source ↗Who it is built for
One assurance language across teams that rarely share one.
Engineering can move faster because security, risk, and release authorities can inspect the same evidence without flattening it into a single opaque score.
AI platform & engineering
Prove that the integrated agent—not only the underlying model—behaves within its intended operating boundary.
Security & red teams
Turn adversarial findings into repeatable scenarios, minimized failures, and enforceable release criteria.
Risk, governance & audit
Review traceable evidence, explicit uncertainty, approvals, conditions, expiry, and revocation in one chain.
Product & release leaders
Make a clear ship, constrain, or stop decision without translating a stack of disconnected technical scores.
Which companies benefit
Built for organizations where agent failure becomes business risk.
The strongest fit is not determined by company size. It is determined by the authority the agent receives and the cost of being wrong.
BEST-FIT SIGNALYour agent can write, transact, decide, access private data, or operate without immediate human review.
Regulated operators
Financial services · Healthcare · Insurance · Public sectorAgents touch protected data, regulated decisions, or workflows where due diligence must be demonstrated—not merely asserted.
High-volume digital businesses
Commerce · Marketplaces · Telecom · Enterprise SaaSA small failure rate can become thousands of customer-impacting actions when agents operate at scale.
Critical operations
Energy · Manufacturing · Logistics · InfrastructureAgent actions can affect uptime, physical processes, access control, and incident recovery under incomplete information.
AI vendors & integrators
Agent platforms · Model providers · Consultancies · System integratorsEnterprise buyers need portable assurance evidence tied to the exact system they are being asked to trust.
Where assurance becomes tangible
Find the failure boundary before production does.
Can the agent resist a poisoned payment instruction during an approval outage?
Compose supplier spoofing, stale ERP data, permission loss, and retry behavior in one reproducible scenario.
Does revoked authority remain revoked across memory, tokens, and tool sessions?
Probe credential refresh, delegated access, cached permissions, and fail-closed behavior at every boundary.
What happens when a recovery agent’s dependency view is incomplete?
Explore counterfactual actions, containment time, residual state, and required human intervention before granting write access.
The assurance architecture
Six connected layers. One inspectable decision.
Every layer has a narrow contract. The interface is optional; sealed bundles and verification artifacts remain portable.
Read the architecture ↗World
Versioned enterprise state, actors, resources, clocks, and uncertainty.
Scenario
Typed events, invariants, faults, adversaries, and termination rules.
Execution
Seeded harness, capability mediation, protocol emulation, and replay.
Evaluation
Independent oracles, causal traces, uncertainty, and failure minimization.
Evidence
Content-addressed manifests, signatures, approvals, and verification exports.
Assurance
Threshold gates, residual risk, deployment conditions, expiry, and revocation.
Assurance intelligence
Keep the control system current.
Original operator guidance plus a daily primary-source watch desk. The pipeline records provenance and refuses to invent news when source retrieval fails.
01
Release assurance · 7 min
↗02The release artifact enterprise agents are missing
A model score cannot tell a release authority which exact agent configuration was tested, where it fails, or what authority it may safely receive.
System evaluation · 8 min
↗03Why model evals miss agent authority risk
The most expensive failures occur where a plausible model response becomes an unauthorized tool action, stale decision, or irreversible business event.
Engineering practice · 6 min
↗Reproducing agent failures from sealed inputs
A finding becomes actionable when an engineer can replay the same world snapshot, seed, event schedule, and dependency behavior—and verify the same trace root.
Evidence before authority
Your agent is asking for access.
Make it earn trust first.
Run the public campaign, inspect every artifact, then take the open architecture into your preferred environment.