Four systems, each solving a different half of the same problem.

A governed control plane for AI agents

A private control tower that lets a company run AI coding agents from chat apps like Slack or Telegram — with rules about who may do what, a full audit trail, and one-click deployment of whatever the agents build.

First-party code

182,600 lines

Source: git ls-files across .py/.ts/.tsx/.js/.mjs/.sh, excluding one vendored bundle directory

Backend service modules

53

Source: ls control/plane/src/server/services/

Python test functions

643

Source: grep 'def test_' across tests/

• Two-plane authorisation: 15 control surfaces × 4 account groups × per-runtime capability policies, so a lower-privilege human can drive a higher-capability agent without escalation. Append-only audit log; TTL-scoped cross-organisation delegation.
• Gauntlet, a four-track agent-harness benchmark on UK AISI's Inspect AI composing eight public datasets, running safety probes inside a network-blocked sandbox against a fabricated decoy. Measures harness delta rather than absolute model score, because scaffold choice can move results further than the model does.
• Formal adversarial review of the agent wrapper with every finding closed and regression-tested: a normalised command classifier resistant to several evasion classes, a fail-closed policy bridge and approval gate, and an OS-level sandbox backstop.
• Production surface: Terraform-provisioned hosts, 13 Docker Compose stacks, 5 CI/CD pipelines including a fully automated provisioning lane with no operator and no agent in the loop, and OpenTelemetry tracing correlated browser-to-server.

• TypeScript
• Python
• React
• Node
• Docker
• Terraform
• OpenTelemetry
• Inspect AI

Autonomous research platform

An online platform where AI agents actually run a scientific investigation end to end — planning it, executing code in isolated sandboxes, checking their own work against strict quality contracts, and publishing a finished, peer-reviewable paper.

Application code

613,600 lines

Source: git ls-files across .py/.ts/.tsx

Tests

8,100 Python + 3,900 TS

Source: grep 'def test_' and it(/test( across the tree; 80% coverage gate

Services

9

Source: docs/v3/ARCHITECTURE.md service table

• Designed the run engine: a deterministic render → turn → parse → validate → repair phase loop that binds every model output to a per-phase schema, classifies failures into a six-value verdict, and auto-repairs within bounded attempts before escalating to a human.
• Built spend governance into the run lifecycle — payment pre-authorisation before dispatch settled by capture-or-cancel through a single idempotent finaliser, plus a cross-replica model throttle and a token-budgeted context packager.
• Shipped an autonomous supervisor that detects interrupted runs, decides with an LLM, and applies a typed intervention or escalates — with row-level locking so a supervisor and a human cannot double-apply the same fix.
• Enforced architecture mechanically rather than by review: layer import direction checked in CI, consumer-driven contract tests that load the producer's real payload builder, and a harness that turns any skipped or empty test tier into a build failure.

• Python
• FastAPI
• Next.js
• PostgreSQL
• Redis
• Azure
• Terraform
• Pydantic

Open-source neuro-symbolic framework

An open-source framework that combines language models with mathematical solvers, so a complex task breaks into small steps that can each be checked for correctness instead of trusted.

GitHub stars

1,745

Source: GitHub API, 2026-08-04

Forks

91

Source: GitHub API, 2026-08-04

Citations

49

Source: Google Scholar, 2026-08-04

• Author and maintainer. Composes generative models with symbolic solvers over an expression graph, decomposing a complex task into small operations that are individually verifiable and recombinable.
• Published at CoLLAs 2024. Recognised publicly by Gary Marcus, Sepp Hochreiter, Jim Fan, an IBM Executive Architect and Microsoft's Data & AI Product Lead.
• Covered by MarkTechPost, Science Times, Montreal.AI and mind-verse.

• Python
• Z3
• SymPy
• WolframAlpha
• BSD-3-Clause

Human-centric AI publishing

A writing tool built with the publishing platform story.one that guides an author from research to a finished book through six checked stages, instead of generating a draft and hoping.

Partnership

18 months

Source: The Inflection Point, ExtensityAI blog, Sep 2025

Launch

Frankfurt Book Fair 2025

Source: PR Newswire, Sep 2025

• Led the AI architecture productising the neuro-symbolic stack into a six-stage authoring workflow — search, read, summarise, write, improve, finalise — with structured outputs, cross-reference consistency checks and human-in-the-loop review.
• Defined abstract editorial concepts such as writing style and tonality in terms a machine could execute without conflicting instructions.

• Python
• SymbolicAI
• LLM orchestration