Playbook Protocol (v0.1)

Playbook Protocol is an open, language-agnostic specification for defining, validating, coordinating, and optimizing any multi-step process as a declarative playbook.

Why We’re Here

Modern automation lives everywhere—from cloud APIs and databases to IoT sensors, AI agents, and robotic arms. While powerful, coordinating these systems introduces significant challenges:

• Brittle Imperative Logic: Traditional scripts break easily when steps fail or underlying tools change.
• Testing as an Afterthought: Validating complex outcomes ("Is the invoice actually paid and reconciled?") isn't typically a first-class part of workflow definitions.
• Hidden Costs & Performance: LLM calls, API requests, compute jobs, and agent actions incur real costs and latency, yet tracking and optimizing across tools is inconsistent.
• Lack of a Shared Standard: Teams constantly reinvent DSLs (YAML, JSON, code) for their processes, leading to vendor lock-in, tool silos, and integration friction.
• The Coordination & Integration Challenge: As workflows become more complex, involving parallel execution branches, multiple agents, or generative AI, managing dependencies, ensuring consistency, and intelligently merging divergent results becomes exponentially harder.

Playbook Protocol addresses these gaps by providing a standard way to define, execute, and understand complex workflows.

What Playbook Protocol Offers:

1. Declarative Playbooks (as DAGs): Define your process as a Directed Acyclic Graph (DAG) of named stages. This structure naturally supports complex dependencies and parallel execution paths. Each stage clearly defines:
   • Expectations (expect:): Testable conditions that must be true for the stage to succeed. Acts as built-in validation.
   • Metrics (metrics:): Track performance, quality, or any quantifiable outcome (e.g., latency, success rate, accuracy).
   • Costs (cost:): Monitor resource usage – API credits, LLM tokens, compute time, estimated dollar amounts, etc.
2. Inspect & Advance RPCs: A minimal RPC interface (inspect, advance, evaluate) allows diverse engines (code, CI, AI agents, low-code tools) to interact with and drive playbook execution based on the current state and defined logic.
3. Deep Observability & Provenance: Automatically capture detailed state snapshots, logs, metrics, and costs for every stage transition across all runs. This rich execution history enables:
   • Visualization & Debugging: Understand exactly how a workflow executed, pinpoint failures, and analyze bottlenecks.
   • Programmatic Analysis: Query the state and history of any run or artifact.
   • (Planned) Graph Utilities: Leverage the DAG history for diffing execution branches, finding common ancestors (LCA), and understanding artifact provenance – crucial for managing parallel work and enabling intelligent merging/conflict resolution.
4. Bulk Simulation & Optimization: Run playbooks at scale on real or synthetic inputs. Evaluate runs against reward-per-cost functions to tune parameters, compare strategies (e.g., different LLM prompts), and optimize for business KPIs.

Core Principles

• Vendor-Neutral: An open specification. Use any compliant engine or SDK.
• Declarative First: Define what success looks like and what resources are used, not how to execute the steps.
• Test-Driven: expect conditions make validation a first-class citizen of the workflow definition.
• Cost-Aware: Resource tracking is built-in, enabling optimization and financial controls.
• Universally Applicable: Model any process – APIs, databases, LLMs, IoT, robotics, human tasks, business SOPs.
• Extensible: variables hold state, tools represent actions – they can be anything.

Example Playbook (YAML)

# Simple example: Invoice Collection Process
id: invoice-collection-v1
description: Records an invoice and attempts payment within a time limit.
variables:
  invoice_id: { type: string, required: true }
  invoice_data: { type: object } # Populated by fetch tool
  payment_status: { type: string, default: 'pending' } # Updated by charge tool
stages:
  - name: recorded
    description: Ensure invoice data is fetched and marked as recorded in our system.
    expect:
      - invoice_data.id == variables.invoice_id
      - invoice_data.system_status == 'recorded'
    tools:
      - fetch-invoice-from-source # Input: invoice_id -> Output: invoice_data
      - mark-invoice-recorded   # Input: invoice_data
    metrics:
      fetch_latency_ms: { type: duration }
    cost:
      fetch_api_call: { type: count, unit_cost: 0.01 } # Cost per fetch

  - name: attempt_payment
    description: Charge the payment method associated with the invoice.
    depends_on: [recorded] # Only run if recorded stage succeeded
    expect:
      # Check variable possibly updated by the tool
      - payment_status == 'paid'
      # Could also check external state if tool updates it
      # - external_payment_gateway.check_status(variables.invoice_id) == 'paid'
    tools:
      - charge_customer_payment # Input: invoice_data -> Output: updates payment_status variable
    metrics:
      time_to_payment_attempt_secs: { type: duration, max: 3600 } # Metric with threshold
    cost:
      payment_api_call: { type: count, unit_cost: 0.02 } # Cost per payment attempt

Use Cases

• AI Agent Orchestration & Collaboration: Coordinate single or multi-agent systems. Agents use inspect() and expect for reliable decision-making. Leverage DAG structure and provenance to manage parallel agent tasks and resolve conflicts when integrating their work.
• Parallel Experimentation & Optimization: Define playbook branches to A/B test different LLMs, prompts, database queries, or algorithms simultaneously. Use built-in metrics/costs and evaluation logic to automatically select the best-performing path.
• Complex Artifact Generation & Integration: Orchestrate the parallel creation of related components (e.g., code, tests, documentation, infrastructure). Use planned graph analysis utilities to manage dependencies and intelligently merge results based on workflow history.
• Robust CI/CD Pipelines: Replace brittle scripts with declarative, test-driven playbooks that track costs and performance for build, test, and deployment stages.
• IoT & Robotics: Model sensor-read → analysis → actuator-write sequences with built-in validation, latency metrics, and cost tracking (e.g., energy consumption).
• Standard Operating Procedures (SOPs): Codify business processes (support tickets, order fulfillment, compliance checks) as shareable, testable, and continuously optimizable playbooks.

Get Involved

This project is in its early days (v0.1). We welcome feedback, ideas, and contributions:

• Read the spec: playbook-protocol/specification
• Try the examples: SDKs and utilities in our GitHub org
• Discuss: Open an issue or join our community forum
• Contribute: PRs, RFCs, or new integrations (Zapier, AWS, robotics)

Roadmap & Next Steps

1. v1.0 Spec Finalization: Lock down schemas for expect, metrics, cost, and core RPCs.
2. Reference Engines: Implementations (e.g., Node, Rust) of inspect/advance/evaluate.
3. SDK Releases: Clients for TypeScript, Python, Rust, Swift.
4. CLI & Inspector: playctl commands and a web UI for running, inspecting, visualizing DAG executions, and utilizing (planned) graph analysis utilities (diff, LCA).
5. Tooling Ecosystem: Integrations with common platforms (CI/CD, Cloud Providers, AI Frameworks).
6. Industry Playbook Libraries: Curated examples for common use cases.
7. Bulk Simulation Engine: Support for parallel runs and built-in optimization loops.

© 2025 Playbook Protocol • BSD-3-Clause • playbookprotocol.org | GitHub