Engineering with AI agents
Practical guides to building with agents: defining the task, checking the code, and deciding what is ready to ship.
Get the data, MCP & skillsThe agentic code factory: from request to releasable change
A useful code factory produces changes a team can trust. Its unit of work is a releasable change with evidence, an owner, and a recovery path.
Read the article- 1Frame
Agree on intent and acceptance evidence.
- 2Build
Work within an isolated, bounded task.
- 3Verify
Run checks and assemble revision-bound evidence.
Pass → independent review, then authorized release.
Fail → diagnose and retry within budget; otherwise return to the owner.
Engineering guides and workflows
Guides grounded in engineering practice and published research. Each includes a practical workflow, its failure modes, and sources you can inspect.
The agentic code factory: from request to releasable change
A practical design for an agentic code factory: task contracts, isolated execution, evidence, review gates, bounded retries, and release ownership.
Code review after agents: verify the change, not the explanation
An evidence-first review workflow for agent-written code, covering risk routing, independent tests, actionable findings, and exact-revision release gates.
Context engineering: repository knowledge, MCP, and skills
How repository knowledge, task context, MCP tools, and agent skills fit together, with provenance, freshness, trust boundaries, and a practical context contract.
Evaluating agentic engineering: quality, cost, and delivery
A measurement plan for agentic engineering that includes accepted outcomes, human review, rework, failures, cost, and the limits of current productivity evidence.
How we research and publish these guides
Original Vibe Haus synthesis and proposed engineering workflows, informed by the linked primary sources. These articles are not peer-reviewed studies or measured customer results. Examples and thresholds are illustrative unless explicitly attributed. Read our editorial approach.
Inspired by the engineering conversations at AI Engineer, with sources from practitioners, protocol maintainers, and research organizations. Independently authored; no affiliation or endorsement implied.
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