Particularly relevant for AI-assisted fulfillment flows. Python SDK

Great for orchestration, AI workflows, automation, and research-heavy systems.

Python is a natural fit for execution coordination where scripts, automation, and intelligent agents need to manipulate protocol-native missions and evidence.

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What this page covers

Keep protocol objects explicit.

Prefer deterministic lifecycle transitions.

Treat evidence and verification as first-class constructs.

Make extension boundaries visible.

Highlights

Protocol-native entry points for this surface.

Orchestration

Drive multi-step mission workflows in concise, inspectable code.

See AI integration

Automation

Coordinate evidence collection, verification, and event handling.

Review lifecycle

Experimentation

Useful for prototyping extensions and workflow research.

See community

mission = client.missions.create(payload)
client.evidence.attach(mission.id, evidence)
verification = client.verifications.evaluate(mission.id)

Working principles

Keep the protocol stable while implementations evolve.

Keep protocol objects explicit.

Prefer deterministic lifecycle transitions.

Treat evidence and verification as first-class constructs.

Make extension boundaries visible.

Getting started

Use the docs path to understand concepts before writing code.

Open docs

CLI

Pair scripted orchestration with operator-friendly commands.

View CLI

Next step

Useful where humans and AI collaborate.

Because Poiva treats humans and AI as first-class participants, Python-based orchestration can sit naturally in mixed execution environments.