Community · Vendor Neutral · Working Standard
AI Engineering Standard
Scaffold a production AI harness in five minutes — vendor-neutral working standard for AIDLC
Corpus shipped. What remains Draft is industry adoption — external pilots, shared governance, and inter-org critique — not inventing the practices.
npx create-aies-kitMaintained in the open. Attribution: project steward · open standard specification.
Get Started with Documentation
Choose your entry point into the AI Engineering Standard.
Quickstart Guide
Rapidly install IDE-native skills, prompt standards, and stage gate configurations into your project harness with `npx`.
Browse Standard Docs
Explore full interactive documentation with sidepanel navigation covering AIDLC, Evals, Prompt Engineering, MCP, and Org Playbooks.
Quick Paths by Goal & Persona
Skip the wizard — Jump straight to the content most relevant to your objective.
Scaffold IDE harness & launch initial workflow from idea to code.
Understand harness principles & pattern library for safe first PR.
Strangler patterns & controlled AI refactoring specifications.
Practitioner handbook covering prompts, agents, evals & MCP.
Normative controls, JSON schemas & governance crosswalks.
Stack-specific playbooks for Next.js, React, Node, Python & Java.
Token Waste & Cost Savings Calculator
Calculate how much your team saves by applying AIES context allowlists, progressive disclosure & model routing.
AI Engineering Maturity Quiz
Assess where your engineering team lands on the 10-Level AI Maturity Scale in 60 seconds.
1How do you structure context for AI agents or prompts?
2How do you manage agent skills and reusable instructions?
3How do you bound agent iteration loops and API budget spend?
4How do you test prompt & agent changes before deployment?
5What governance & security controls protect your AI harness?
Why AI engineering needs a standard
Teams invent private dialects for prompts, agents, evals, and approvals. The result is unreproducible delivery, silent security gaps, and tools that cannot interoperate. AIES exists so organizations share one vocabulary, one set of gates, and one way to evidence production readiness — without locking to a single model vendor.
“Trust comes from documentation, versioning, and community critique — not from declaring ‘the standard’ on day one.”
Why adopting a standard harness pays for itself at every AIDLC step
Standardizing prompts, context windows, MCP tools, and eval gates eliminates trial-and-error engineering, slashes token bills, and guarantees audit readiness.
Step-by-Step AIDLC ROI Breakdown
Standard vs Ad-Hoc| AIDLC Stage | Ad-Hoc Engineering Risk | AIES Standardized Practice | Quantified Impact |
|---|---|---|---|
| 1. Idea & Architecture | Teams invent private prompt/agent dialects | Standard harness profile (`AIES.md` + `AGENTS.md`) | 50% faster setup |
| 2. Design & Context | Bloated context windows cause lost-in-the-middle decay | 50/50 context layout & token budget rules | 35% token savings |
| 3. Build & MCP Tools | Unrestricted tool execution (indirect prompt injection) | Bounded MCP tool contracts & authorization schemas | Zero OWASP LLM01 gaps |
| 4. Test & Evaluation | Manual spot testing before production deploy | CI/CD quality gates (`aies audit` + Promptfoo) | 42% fewer regressions |
| 5. Deploy & Governance | Manual 6-week security and regulatory binder gathering | NIST AI RMF & EU AI Act machine evidence binding | Audit readiness in 4 days |
Global Framework Crosswalks & Citations
AIES operationalizes code-level controls aligned with recognized international standards and tooling platforms.
Risk Management Framework for trustworthy AI systems — mapped to AIES stage gate verification.
International standard for Artificial Intelligence Management Systems — evidence-first governance controls.
Defenses against indirect prompt injection, sensitive info disclosure, and unverified tool execution.
Mandatory transparency, human oversight, and auditability requirements for high-risk AI models.
Open standard for connecting AI agents to tools, data resources, and prompt context.
Automated prompt testing, red teaming, and CI/CD evaluation gate integrations (Promptfoo, DeepEval, Langfuse).
* All product names, trademarks, and registered trademarks cited above are property of their respective owners. Citations provide informational crosswalks to recognized industry specifications.
Engineering principles
The constitution every AIES artifact should obey.
Community & Contributing
AIES improves through issues, PRs, pilot evidence, and disagreement on the record.