Skip to content

artifacts.md

Latest commit

 

History

History
42 lines (30 loc) · 2.37 KB

artifacts.md

File metadata and controls

42 lines (30 loc) · 2.37 KB

Artifact Typing & Validation

Part of the AgentForge documentation.

Every agent declares typed inputs and outputs. Artifacts are validated against Zod / JSON Schemas at every pipeline boundary — invalid output fails the agent run before it reaches the next phase.

Agent YAML                    Zod Schema                      Runtime
┌─────────────────┐    ┌──────────────────────┐    ┌──────────────────────┐
│ outputs:        │    │ RequirementsSchema   │    │ Agent produces JSON  │
│   - type:       │───▶│   = z.object({       │───▶│ → safeParse(output)  │
│     requirements│    │     epics: [...],    │    │ → pass ✓  or fail ✗  │
│     schema: ... │    │     ...              │    └──────────────────────┘
└─────────────────┘    │   })                 │
                       └──────────────────────┘

What ships

45 built-in schemas covering requirements, architecture, code, data, testing, security, and DevOps — see packages/core/src/schemas/. Every shipped template references them so you can compose pipelines without inventing new artifact types.

Defining your own

Add a Zod schema in TypeScript and reference it from agent YAML by file path:

# .agentforge/agents/my-agent.agent.yaml
spec:
  outputs:
    - type: my-artifact
      schema: schemas/my-artifact.schema.yaml

The schema file can be either Zod-shaped TypeScript (loaded via the schema registry) or a JSON Schema YAML — both validate at the same boundary.

Architectural details and the artifact flow through phases: docs/architecture.md.

Why this matters

  • Malformed LLM output is caught early. Bad JSON, missing fields, or wrong types abort the agent run before downstream agents consume it.
  • Wiring is explicit. Each agent's inputs[].type and outputs[].type form a contract. Two agents producing the same type is a configuration error you catch at agentforge validate time, not at runtime.
  • Schemas double as docs. New team members understand what each phase produces by reading one file.