Assured FDE

Deploying agents is solved. Day 91 is where we start.

Your FDE partner gets agents into production in ten to twelve weeks. Then the data underneath them moves every week. 0to60.AI connects each change in your data estate to the agents it exposes, clears it before release, and verifies the business action afterward.

Book a working session See the 90-day deployment

Know before you ship. Verify after it acts. Built for regulated lenders first, then the financial services, healthcare, and sovereign workflows they generalise to.

70%Agent one

70%Agent two

70%Agent three

34%

End-to-end reliability when three agents at 70% each run in sequence.

Illustrative: 0.7 × 0.7 × 0.7 = 0.34

Delivered with regulated enterprises, cloud marketplaces, and SI partners

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What changed

The FDE industry won deployment. Nobody owns what comes after.

Solved

Deploying agents

Platform-native builders ship free, and a funded FDE industry stands production agents up in ten to twelve weeks.

Crowded

Basic governance

AI gateways and governance platforms already cover identity, policy, routing, inventory, and tracing.

Unmapped

Everything after

The agent is live, the data underneath it moves weekly, and nobody can say which change breaks which business decision.

Every agent CI gate on the market fires on the agent's own repo. Ours fires on the warehouse.

A prompt commit or a model swap triggers today's gates. A refactored dbt model, a redefined business metric, or an updated approval policy does not. And those gates score how the answer looked, not the transaction that landed in the system of record.

The seam

The change starts on the left. The damage shows up on the right.

The teams that own the left don't know which agents consume their definitions. The teams that own the agents don't watch the warehouse. Nothing crosses that seam, so the failure surfaces as a wrong business action that nobody logged as an error.

Two March 2026 research papers introduced regression testing and pre-change impact analysis for agents, and both said the method didn't previously exist. The problem is now named and being solved for code and prompts. Nobody has taken it upstream to the data estate.

Sources: AgentAssay and TDAD preprints, March 2026; Snowflake, Databricks, and LangChain documentation, 2026.

Why now

Supervisors now expect agent-specific testing, monitoring, and action records.

FINRA's 2026 Annual Regulatory Oversight Report sets out supervision, recordkeeping, testing, and ongoing-monitoring expectations for agentic systems that execute multi-step tasks. It doesn't require an independent vendor to clear each agent. It requires evidence that testing, monitoring, and controls exist.

That evidence is what the 0to60.AI platform produces, on every change.

Sources: FINRA 2026 Annual Regulatory Oversight Report; Regulation (EU) 2026/1744.

And a fixed European clock behind it

2 Aug 2026Article 50 transparencyLive now
2 Dec 2026New prohibitionsPlus watermarking for legacy systems
2 Dec 2027Annex III high-riskDeferred by the Digital Omnibus
2 Aug 2028Annex I high-riskAI embedded in regulated products

What the pod installs

Embedded delivery. A platform left running.

A small senior team works inside your release and risk functions, installs the 0to60.AI platform against your data, controls, and systems, and hands it over. No parallel stack, and no dependency on us to keep it running.

Embedded principals

Senior people who sit inside your release and risk functions and shape the work to your situation.

Assurance engineers

Data, semantic, and agent engineering on one senior team. No pyramid, and no slide-to-build handoff.

The 0to60.AI platform

The assurance graph, contracts, change gate, and outcome ledger, pre-built. Installed in your environment, in-region or on-premises.

Assurance graphSHIPPED

Consumes the lineage your platforms already own and extends it with agent, tool, policy, action, and outcome edges.

Assurance contractsPILOT

Business requirements compiled into executable data tests, agent evaluations, policy checks, and outcome tolerances.

Change gateROADMAP

Detect a change, identify exposed agents, replay only the affected scenarios, then block, canary, approve, or remediate.

Outcome ledgerROADMAP

Reconcile the action against the system of record. Was the right refund issued? Did the transaction match the approval?

Labels are honest. The dependency mapping and test generation underneath the graph run with customers today.

Before it ships

Upstream change proposedA dbt model, metric definition, prompt, model, or policy changes.
Blast radius resolvedThe graph names every agent and workflow exposed to it.
Affected scenarios replayedOnly the golden scenarios at risk, not the whole suite.
Verdict issuedBlock, canary, approve, or remediate, on the agreed tolerance.

After it acts

Agent executesThe action lands in the core system or the ERP.
Outcome reconciledChecked against the system of record, not how it sounded.
Evidence recordedTesting, monitoring, and action records for supervisors.
Scenarios updatedReal outcomes become the scenarios tested next time.

One loop, not two products. Reconciliation sets the standard the gate enforces next time.

The land motion

The 90-day agent assurance deployment.

One consequential workflow, taken end to end. The pod installs reusable components rather than custom code, then leaves them running.

Map data, semantic, tool, and policy dependenciesSHIPPED
Establish golden business scenarios with the ownerSHIPPED
Compile assurance contracts from those requirementsPILOT
Install change-impact testing into the release pathROADMAP
Reconcile production outcomes to the system of recordROADMAP
Produce the evidence package for supervisorsPILOT

"If we ship this change, which agents break and which business outcomes are exposed?"

Today

Weeks of manual tracing across three teams, or no answer at all until a customer finds it.

With the 0to60.AI platform

Hours, on every change, with a recorded verdict and the evidence attached.

Beside your FDE partner

They build and deploy. We grade the work, not the vendor.

Keep the hyperscaler, lab, or integrator you chose. Their pod installs the build. Ours installs the assurance substrate on top of what they shipped, so the agents they hand over stay provable after handover.

Contracts, attestations, and the change-to-outcome record live in your environment and stay with you if you ever change builders, models, or platforms.

Pricing

Priced per consequential workflow, not per agent.

Agent counts multiply and collapse without warning. Workflows are stable, owned by a named business person, and already carry a risk rating.

90-day deployment

Fixed fee per workflow. One consequential workflow, end to end, with reusable components installed.

Assurance platform

Annual, per workflow under assurance. Expands by workflow and by agent fleet.

Evidence retainer

Annual, per regulated entity. Testing, monitoring, and action records on the supervisory calendar.

What we measure with you: workflows covered, tests generated, changes assessed, release-certification time, escaped failures, manual review time reduced, and the share of consequential actions reconciled.

Where we start

Built first for lending, where a changed definition is a compliance event.

A revised affordability rule, a refactored income model, or a new bureau field can change credit decisions across every agent in origination, servicing, and collections. We start where an unverified change costs the most, and the change-to-outcome record compounds across the vertical.

Illustrative assurance contracts shown. Deployable in-region and on-premises for Gulf and African programmes.

pass

contract: affordability_definition

on change: metric.affordability

re-evaluate: origination agents

bar: approval rate and fairness within tolerance

block

contract: hardship_policy_scope

agent: collections.arrangements

must not: propose terms outside policy v4.2

on violation: block release

reconcile

contract: preapproval_outcome

reconcile: pre-approvals vs core system

against: the approval on record

variance over tolerance: open review

Where we fit

Crowded at every layer. Fragmented across the chain.

	FDE and SI partners	Agent evals and CI/CD	AI governance and GRC	0to60.AI
Primary job	Integrate and deploy the agent	Trace and score agent behaviour	Inventory, policy, approvals	Assure the change-to-outcome chain
What triggers a check	Project milestones	A change in the agent's own repo	An approval workflow	Any upstream change: data, semantics, prompts, models, tools, policy
What gets scored	Delivery acceptance	Output quality	Policy conformance	The action in the system of record
After handover	Engagement ends or renews	Dashboards	Registers and attestations	A running gate and a growing evidence record
How we work together	They build, we assure	We consume their signals	We feed their registers	Complements all three

Questions buyers ask

Are you another FDE firm?

No. FDE firms build and deploy agents. We deliver with an embedded pod too, but what we install is the assurance layer that keeps those agents provable after the build team hands over.

Do we have to replace our current AI partner?

No. We sit beside the hyperscaler, lab, or integrator you already chose. They keep building; we connect their work to your data estate and systems of record.

How is this different from LangSmith, Braintrust, or Arize?

They're strong at what they do, and we use their signals. Their gates fire on a change in the agent's repo. Ours fires on a change in the warehouse, the semantic layer, or policy, and scores the transaction that landed, not how the answer looked.

Does FINRA require an independent vendor?

No. It expects evidence that testing, monitoring, and controls exist for agentic systems. The platform produces that evidence on every change, and packages it for supervisory review.

What do we get in 90 days?

One consequential workflow under assurance end to end: dependencies mapped, golden scenarios agreed with the owner, contracts compiled, and an evidence package. Capabilities still on the roadmap are labelled as such in the plan.

Where does it run?

In your environment, on your platforms, including in-region and on-premises deployments.

When does an engagement end?

At handover. The platform is left running inside your release path, and your team operates it without us.

Bring one consequential workflow.

Ninety minutes with two of our principals. We'll map what the workflow's agents depend on, what could change underneath them, and what evidence your supervisors would expect. You leave with a 90-day deployment plan, whether or not you work with us.

Book a working session Or email the team