AI writes code faster than your team can trust.
An AI agent emits a stream of output: fast, prolific, and mostly wrong. Five failure classes keep showing up: duplication and verbosity, hallucinations, unreviewed handoff, non-functional failures, missing acceptance tests. All of it lands on a single engineer who burns out trying to keep up.
The sources of value worth protecting are your attention, your output correctness, and your action safety. Without a harness, AI generates faster than your team can trust.
A harness around the agent.
The solution wraps the agent in a complete pipeline: context feeds the agent, workflows drive execution, verification checks every output, and user review gates the final decision. A knowledge base compounds underneath, feeding every session and drawing from it.
Pluggable modules handle the edges: skills, integrations, tools. Verification sits in the middle because every output gets checked before it reaches anyone. Users stay in the loop at every gate. Nothing stays in the session.
Context engineering
AGENTS.md is where context discovery starts, the entrypoint that points the agent to your knowledge base, your session, and external sources. A learning loop captures what happened in each session, consolidates it, and feeds it back as context for the next one. The knowledge base compounds: decisions, precedents, docs, all stacking up.
External sources plug in through a catalog: GitHub, Figma, Notion, CI, so the agent doesn't work in a vacuum. Every session makes the next one better. Nothing gets lost between sessions; everything becomes part of the living system.
Work is a pipeline, not a chat.
Nine steps: intake, spec design, architecture and infrastructure, implementation plan, coding, agent-led verification, assisted user review, deployment and observation, knowledge capture. Custom skills drive each step. Runtime hooks enforce compliance. Hand-off checkpoints appear at the right time where users take over.
At step 5, the pipeline fans out to parallel subagents working in isolated git worktrees, that's where the speed comes from without sacrificing correctness. The agent runs its own verification next. Knowledge capture feeds everything back to intake. The knowledge base keeps compounding.
Two-way knowledge transfer.
The agent offers walkthroughs and visual explainers. The engineer explains intent, tradeoffs, and values. Five artifacts anchor every action: decision records, ADRs, specs, tradeoff docs, walkthroughs, nothing lives only in the session.
An adversarial review agent attacks the diff before engineers read it, catching assumptions, oversights, the failure modes engineers would miss. The review queue collapses from five PRs to one. Understanding is the true cost of speed, and this engine pays it.
Vibing not allowed.
The spec is the contract. The diff is the change. The agent writes the test suite, executes it, and produces proof. Then every output passes through a gate chain: security analysis, quality checks, and compliance verification, GDPR, PCI, HIPAA, SOC 1 and 2, custom frameworks.
Fail any gate and it's blocked, back to coding. Pass all of them and you get verified: proof attached, every change carrying its evidence. This is how AI becomes safe for mission-critical software.
The core stays. The edges are yours.
The core harness stays fixed, skills, integrations, and custom tools all plug in through sockets. Skills handle onboarding, workflow steps, knowledge base maintenance, learnings, TDD, parallel agents. Integrations connect to tasks, docs, design, Git, CI/CD, and observability. Custom tools handle deterministic work like PR lifecycle and pipeline status.
The whole system is model- and runtime-portable, encoding team standards without locking you to any vendor. As the model landscape shifts, your edges stay yours. Bad practices get prevented by the structure itself.