Hub 03 · Durable execution & repository authority

Distributed execution for autonomous coding agents.

Parallel software workers only remain safe when code identity, durable state and write authority are explicit. Nexus combines Git’s immutable history with database-backed authority, fenced execution and role-aware deterministic scheduling.

How can multiple AI coding agents work safely?

Multiple agents can work safely when ownership, repository identity, evidence and integration authority live outside the models. Nexus preserves those invariants through durable project state, fenced execution, independent review and one governed canonical outcome.

01

Technical article

Building a Git-Native Software Production Line with PostgreSQL

Git stores immutable code history; PostgreSQL stores durable delivery authority. Nexus binds work, evidence, review and integration decisions to exact repository identities while keeping project state recoverable and auditable. Neither system is asked to impersonate the other.

  • Git for code identity and history
  • PostgreSQL for durable workflow authority
  • Evidence bound to repository SHA
  • One integration authority advances canonical state
02

Technical article

Fenced Ownership for Recoverable Agent Execution

A timeout alone cannot stop a displaced worker from completing late. Nexus gives active work an authoritative ownership generation and accepts transitions only from the currently authorised execution context. Recovery can therefore continue without trusting an expired process to disappear cleanly.

  • Exclusive, durable work ownership
  • Stale execution rejected
  • Crash and restart recovery
  • Fail-closed state transitions
03

Technical article

Deterministic Scheduling for Autonomous Engineering

Nexus applies deterministic, role-aware scheduling across build, review and remediation work. The control plane balances responsiveness, fairness and forward progress while preventing starvation, duplicate ownership and stale execution. Scheduling policy remains separate from the AI models performing the work.

  • Starvation-resistant progression
  • Intelligent remediation reprioritisation
  • Role-aware workload policies
  • Model-independent scheduling authority
AUTHORITYDurable ownership

One authorised execution context for each active unit of work

POLICYRole-aware scheduling

Build, review and remediation progress according to governed policy

OUTCOMECanonical progress

Stale or duplicate execution cannot become accepted project state

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