I’ve been building an architecture called SROS, aimed at making AI-assisted work more governable in legal, financial, compliance, and other high-consequence environments.

I’m not posting this as a launch or sales pitch. I want informed criticism before I harden the architecture further.

The core problem I’m trying to solve is this:

Most AI governance discussions focus on policies, model restrictions, risk classifications, or human oversight in the abstract. But when AI is used inside an actual workflow, organizations still need to answer:

• What information entered the system?
• Which sources supported the output?
• What assumptions were made?
• Which controls were triggered?
• What uncertainty remained?
• Who approved the final action?
• Can the entire process be reconstructed afterward?

SROS treats governance as part of the execution path, rather than a document placed around the system.

Current architecture

A governed workflow moves through several stages:

1. Intake and risk classification

The request is classified by workflow, jurisdiction, sensitivity, consequence, and required approval level before execution begins.

2. Controlled context assembly

Relevant documents, policies, sources, instructions, and restrictions are assembled into a bounded context. Missing information must be surfaced rather than silently inferred.

3. Role-separated execution

Research, generation, inspection, red-teaming, and approval can be handled as separate stages rather than allowing one model response to perform every function and certify itself.

4. Policy and control gates

Outputs are checked against workflow-specific requirements such as source provenance, citation coverage, unsupported claims, confidentiality boundaries, prohibited actions, and escalation conditions.

5. Human decision points

Human review is positioned at defined control points based on consequence and uncertainty, not added as a generic “human in the loop” statement.

6. Evidence pack generation

Each run should produce an evidence package containing:

• Inputs and document versions
• Sources and provenance
• Material claims
• Control results
• Detected uncertainty
• Human approvals or overrides
• Failure and escalation records
• Final output version

7. Immutable run receipt

The system records what happened, which controls executed, what passed or failed, and which person or system authorized the next step.

8. Evaluation and failure capture

Failures should become reusable test cases. The architecture is intended to maintain regression suites, adversarial tests, retrieval-miss tests, and workflow-specific evaluation gates.

Initial workflow targets

I’m currently designing around four concrete workflows:

1. Legal document risk review
2. AI-assisted research and filing risk review
3. Financial report and advisory evidence generation
4. AI governance and client communication review

The intended boundary is important: SROS is not supposed to make final legal, financial, or regulatory decisions autonomously. It is meant to make AI-assisted execution more inspectable, bounded, reviewable, and defensible.

Principles I’m trying to preserve

• No fabricated certainty
• No hidden retrieval failures
• No system certifying its own work without independent inspection
• No high-consequence action without explicit authorization
• Every material claim should be traceable
• Overrides should be recorded, not silently accepted
• Governance controls should be testable
• A successful output is not the same as a successful governed process

Where I want criticism

I would especially value feedback on:

• Where this architecture is naive or incomplete
• Which controls would matter most to auditors, regulators, legal teams, or enterprise risk teams
• Whether the evidence-pack approach would create useful assurance or mostly compliance theatre
• Which parts are overengineered
• What threat models I am missing
• What would need to be demonstrated before you would trust a pilot
• Where human approval creates false confidence rather than genuine governance
• How you would test whether the governance layer actually reduces risk

I’m deliberately avoiding the claim that this “solves AI governance.” My current hypothesis is narrower:

Governance becomes more credible when policies, controls, evidence, escalation, and authorization are embedded directly into the workflow and produce verifiable receipts.

Where does that hypothesis fail?