ASOS Control Room

The description states that ASOS Control Room is a system designed to govern AI agents in operational environments. It turns an incident into a "governed, auditable case" using:

• GPT-5.6 for interpreting evidence and recommending remediation.
• An "ASOS Kernel" that enforces roles, authority, approvals, execution eligibility, and case transitions.
• A deterministic workflow: OBSERVE → PROPOSE → HUMAN AUTHORIZATION → APPLY → INDEPENDENT VERIFY.
• Structured outputs from GPT-5.6 to constrain model responses.
• A React-based interface with Tailwind CSS and Cloudflare deployment.

The system is described as a vertical slice of a larger "AIBRY Specialists OS", intended to support specialist workflows in operational contexts like incident response, but currently limited to one demonstration incident.

Inference The product appears to be an experimental framework for managing AI agent authority in safety-critical environments. It does not appear to be a commercial product or service yet.

The author claims that ASOS Control Room addresses a gap in current AI agent safety: “instructions are not authority.” It positions itself as a way to allow AI specialists to contribute meaningfully without silently expanding their own authority, approving their own recommendations, or executing unverifiable changes.

It also states the goal of creating an "enforceable operating model where reasoning remains flexible, but authority remains deterministic."

The project evolved from a practical need encountered while building and maintaining applications at ASOS. The author frames it as more than just another AI assistant — it's a system for enforcing separation of duties and auditability in agent-based workflows.

The description does not explicitly name target customers or define an ideal customer profile (ICP). However, it implies that the system targets organizations with operational needs where AI agents interact with real systems — particularly those requiring governance over agent actions.

It suggests use cases in incident response, deployment regressions, worker-queue backlogs, synchronization failures, and catalog consistency issues. The focus is on environments where "AI specialists" operate within a structured authority model.

Inference Likely targets large tech companies or infrastructure teams with complex systems and high stakes around AI agent behavior.

There is no evidence of pricing, business model, or monetization strategy in the description. The project was built as part of a hackathon submission and has no stated revenue streams or customer acquisition plans.

Not evidenced

The system uses:

• GPT-5.6 via OpenAI API
• Structured outputs to constrain model responses
• TypeScript for core domain implementation
• React, Next.js, Tailwind CSS for UI
• Cloudflare infrastructure
• Git and GitHub for version control
• Codex for code inspection and generation

It includes:

• Deterministic reset and replay capabilities
• A fallback mode for when live model access fails
• Clear labeling of live vs. saved responses
• An "Interactive Constitution" that makes authority boundaries visible
• End-to-end workflow simulation including investigation, authorization, execution, verification, and closure

Inference The technical stack suggests a focus on developer tooling and system integration, with an emphasis on auditability and deterministic behavior.

There is no evidence of traction or maturity beyond the hackathon prototype. The description explicitly states:

• No revenue data
• No customer base
• No adoption metrics
• No production usage
• Only a demonstration built for a competition

The project was developed during Build Week and submitted to the OpenAI 2026 hackathon.

Not evidenced

The description does not mention any competitors or direct market positioning. It focuses on solving a problem of AI agent governance rather than comparing itself to existing tools in the space.

Not evidenced

• No commercial traction: The system is presented as a hackathon prototype with no evidence of real-world use.
• Limited scope: The demonstration covers only one incident type and workflow, not a full operating system.
• Unverified claims: All descriptions are self-reported and unverified — there is no third-party validation.
• Unclear scalability: While the author mentions future expansion to more incident types, no roadmap or progress toward that exists in this version.
• Dependency on GPT-5.6: The system relies heavily on a specific model version, which may not be available long-term.

1. What is the actual operational context where this would be used? Is it being tested or piloted in any real environment?
2. How does the ASOS Kernel handle edge cases or unexpected inputs from GPT-5.6?
3. Has the system been tested with multiple AI models, or is it tightly coupled to GPT-5.6?
4. What are the plans for expanding beyond the single incident type demonstrated?
5. Are there any regulatory or compliance considerations that would apply if this were deployed at scale?
6. How does the fallback mechanism work in practice — how often is it triggered and what is the impact on decision-making?