CoBolt · ErupThe governed autonomous SDLC platform.

AI now writes code at machine speed.
Your enterprise still ships at human speed.

Copilots, Cursor, Devin, and friends accelerated the keystroke. They did nothing for the 40–60% of engineering capacity consumed by requirements drift, review backlog, test gaps, security findings, compliance evidence, and modernization debt — the work that actually decides whether software ships.

Three tiers of AI tooling — none of them ship enterprise software end-to-end.

One platform. Two surfaces.
One engine underneath.

CoBolt Erup is an autonomous delivery platform that runs the full software lifecycle as a governed, evidence-producing pipeline. Engineers operate it through CoBolt IDE. Organizations govern it through CoBolt Studio. Both surfaces share the same engine, produce the same artifacts, and enforce the same policies.

Same engine, same artifacts. An action initiated in the IDE produces evidence Studio can audit. A gate configured in Studio binds the IDE's next pipeline run. The two surfaces are operationally identical from the engine's point of view — only the persona changes.

Whether the codebase is one day old — or twenty years old.

CoBolt Erup handles two kinds of starting points with the same platform, same agents, and same evidence pipeline. Only the entry point differs.

Years of engineering. All in production. All source-backed.

Ten stages. Every handoff verified.

This is the pipeline Studio governs. Each stage has gates between roles — visible and operable from Studio, executed by the engine that the IDE opens.

We don't ask agents to behave.
We make misbehavior structurally impossible.

Prompts are advisory. Hooks are physics.

• Fail-closed by default.

  Missing proof, skipped verification, or unknown state halts the pipeline — never warns and continues.

• Census-based, not sampling.

  Every endpoint, every role check, every requirement traced to every test. Never a sample, never an extrapolation.

• Sub-agent write isolation.

  Three-layer enforcement prevents agents from silently mutating state outside their lane.

• Hooks above the LLM.

  Deterministic checks fire before, during, and after every tool call. The model proposes; the hooks decide.

Six governance moves the rest of the market hasn't shipped.

Each tile is an enforcement mechanism running today on every CoBolt project — not a roadmap aspiration. Industry comparison reflects open and major commercial agentic-SDLC tooling as of Q2 2026.

Reverse-engineer legacy systems into forward-engineerable specifications.

Stack-specific extractor coverage detail under NDA.

Auditors don't want promises.
They want artifacts.

• Most AI delivery tools treat compliance as something you reconstruct after the fact — pulling logs, hunting for evidence, writing narratives.
• We flip that. The pipeline emits audit-grade evidence as it runs — requirement traceability, gate logs, exploit-verified fixes, authorization coverage, mutation scores.
• That evidence maps directly to SOC 2, ISO 27001, HIPAA, EU AI Act, and FedRAMP control families.
• The line: Zero post-facto reconstruction. Evidence exists before the audit asks for it.

The only platform in the high-autonomy + high-governance quadrant.

Meet your security posture without compromising capability.

Faster cycles. Higher confidence. A shorter path to audit.

Where we are today, in plain language.

The window for governed autonomy is open right now.