Agent Floor

The description states:

• Agent Floor is a zero-dependency Node.js control layer for governed Codex delegation.
• It operates before execution, by rejecting full-history delegation, creating clean worker packets, projecting allowlisted files, and limiting depth, fan-out, cycles, runtime, output, and authority.
• It operates after execution, by attributing only unique request-local usage, separating cached/fresh input/output, verifying file hashes, checking semantic support of conclusions, and admitting or rejecting results at the parent boundary.

Inference This is a technical control layer intended to manage AI agent behavior in a sandboxed way, not a product for end-users or commercial deployment. It is described as a runner and accounting engine, not an application or service.

The description states:

• Agent Floor was built to govern boundary issues in Codex delegation.
• It addresses the problem of inherited cumulative history causing token overuse (555M vs 1.5M tokens).
• The system is positioned as a solution for request-local accounting, clean context, and evidence-based admission.

Inference The positioning appears to be technical and niche — aimed at developers or teams managing AI agents, not end-users. It is framed as a control mechanism, not a product with commercial appeal.

The description states:

• Agent Floor is for users of Codex delegation who want to control token usage and validate agent outputs.
• It targets developers or teams working with AI agents that may inherit full histories, leading to overuse and false attribution.

Inference The target customer is likely technical users, such as developers or engineering teams building or managing AI agents in a controlled environment. No specific ICP is named.

The description states:

• Agent Floor is part of a hackathon submission and not described as a commercial product.
• It is read-only in its current release (AF-G0).
• There is no mention of pricing, licensing, or monetization.

Inference No evidence of a business model or pricing structure exists in the description. The system appears to be an open-source or demo tool, not a commercial offering.

The description states:

• Built with codex, GPT-5.6, javascript, jsonl, node.js, terra.
• Uses Codex non-interactive JSONL interface and deterministic Node.js test/demo path.
• Implements a runner and accounting engine, sanitized regression fixtures, and an evidence-admission system.
• The public demo requires no model call or Codex authentication.

Inference The technical stack is focused on AI agent control, sandboxing, and deterministic execution. It shows a strong focus on security and accountability in AI workflows.

The description states:

• 14/14 automated tests pass.
• AF-G0 passes from a fresh public clone.
• fork_turns="all" is rejected before execution.
• Token discrepancy corrected (372×).
• Public release has zero runtime dependencies and zero npm vulnerabilities.

Inference There is no evidence of customer adoption, revenue, or usage beyond the demo and test suite. The maturity level appears to be that of a proof-of-concept or prototype, not a production-ready system.

The description states:

• Agent Floor addresses problems in Codex delegation.
• It is built to solve token overuse and false attribution issues.
• No direct competitors are named.

Inference There is no evidence of competitive landscape or market positioning beyond the stated problem. The system may be addressing a niche within AI agent control, but no known tools or products are referenced.

The description states:

• Agent Floor is intentionally read-only in its current release.
• It is a hackathon submission, not a commercial product.
• No evidence of real-world usage or adoption.

1. What real-world use cases have you tested this system in?
2. Has it been used beyond the demo and test suite?
3. Are there plans to support mutation or broader telemetry adapters?
4. How does it integrate with existing AI agent platforms or workflows?
5. Is there any intention to commercialize this tool, and if so, how?