AI-as-Code

Wave brings Infrastructure-as-Code principles to AI workflows. Define your AI pipelines declaratively, version them in git, and run them with the same rigor you apply to infrastructure.

The Evolution of X-as-Code

The industry has progressively codified operational concerns:

Era	Paradigm	Tools
2000s	Infrastructure-as-Code	Terraform, Pulumi, CloudFormation
2010s	Configuration-as-Code	Ansible, Chef, Puppet
2016+	Policy-as-Code	Open Policy Agent, Sentinel
2018+	GitOps	ArgoCD, Flux
Now	AI-as-Code	Wave

Each evolution brought the same benefits: version control, reproducibility, collaboration, and audit trails. AI workflows deserve the same treatment.

Why AI Needs the Same Treatment

AI outputs are non-deterministic by nature. Without guardrails:

• Chat history is not version control — Prompts drift, context gets lost, and successful patterns disappear
• Copy-paste prompts don't scale — Teams can't share, review, or iterate on workflows
• No reproducibility — The same task produces different results each time
• No audit trail — When something goes wrong, there's no trace to investigate
• No permission boundaries — AI agents have unbounded access to your codebase

Enterprise adoption requires the same predictability we expect from infrastructure.

Wave's AI-as-Code Principles

Wave implements six core principles borrowed from Infrastructure-as-Code:

1. Declarative Pipelines

Define what you want, not how to get there. Your pipeline is a YAML file that describes steps, dependencies, and contracts.

kind: WavePipeline
metadata:
  name: gh-pr-review

steps:
  - id: analyze
    persona: navigator
  - id: review
    persona: auditor
    dependencies: [analyze]

2. Version Controlled

Pipelines live in git, not chat history. You can:

• Review pipeline changes in PRs
• Roll back to previous versions
• Share workflows across teams
• Track who changed what and when

3. Contract Validation

Every step validates its output against a schema before the next step begins. Malformed outputs trigger retries or halt the pipeline — no garbage in, no garbage out.

output_artifacts:
  - name: analysis
    path: .wave/output/analysis.json
    type: json
    contract: contracts/analysis-schema.json

4. Fresh Memory Isolation

Each step runs with completely fresh context in an ephemeral workspace. No context bleed between steps means:

• Predictable behavior regardless of history
• No accidental information leakage
• Each persona sees only what it needs

5. Git-Native Workflows

Wave integrates with your existing git workflow:

• Initialize with wave init in any repo
• Pipelines are just YAML files in .wave/
• Artifacts are git-friendly

6. Observable Execution

Complete audit trails with credential scrubbing:

• Every tool call logged
• Execution traces for debugging
• Permission decisions recorded
• No sensitive data in logs

IaC Principle Mapping

IaC Principle	How Wave Implements It
Declarative	YAML pipeline definitions, not imperative scripts
Version controlled	Pipelines live in git, not chat history
Reproducible	Contract validation ensures consistent outputs
Idempotent	Fresh memory at every step boundary
Auditable	Complete execution traces with credential scrubbing
Reviewable	PR your AI workflows like any other code

Comparison with Alternatives

Wave's approach differs from other multi-agent tools:

	Wave	Gastown	Claude Flow
Declarative pipelines	YAML	JSON/TOML	Programmatic
Version controlled	✅	✅ (git worktree)	❌
Contract validation	✅	❌	❌
Step isolation	Fresh memory	Shared context	Shared memory
Permission scoping	Per-persona	❌	❌

Gastown

Multi-agent workspace manager with Mayor/Polecat architecture. Strong git integration with worktree-based persistence. Different philosophy: persistent shared state vs Wave's fresh-memory isolation.

Claude Flow

Agent swarm orchestration with 60+ agents and MCP tools. Optimized for parallel execution and collective learning. Different philosophy: shared knowledge base vs Wave's contract-validated handoffs.

Raw Claude Code

Direct LLM interaction. Great for ad-hoc tasks. Wave adds structure for repeatable, team-scalable workflows.

Getting Started

Ready to bring Infrastructure-as-Code rigor to your AI workflows?

1. Quickstart Guide — Get Wave running in 5 minutes
2. Pipelines Concept — Deep dive into pipeline structure
3. Use Cases — Real-world examples