Agent Contracts: Building Reliable Multi-Agent Systems with Contractual Design

Agent Contracts is a declarative YAML DSL toolkit that provides design-time guarantees for AI Agent systems by defining, validating, and rendering multi-agent development workflows—similar to the OpenAPI specification for the multi-agent domain. It focuses on solving governance dilemmas in multi-agent systems, filling the gap in existing frameworks regarding design-time guarantees, and offering static assurance for system structural correctness.

As AI Agent technology matures, multi-agent systems are moving toward production, but they face governance challenges: ambiguous Agent responsibility boundaries, shifting handover rules in prompts, unclear product ownership, inconsistent validation logic, etc. These issues worsen exponentially as the scale expands. Most existing Agent frameworks focus on runtime execution (tool calls, message passing, task orchestration) but ignore structural correctness guarantees in the design phase.

agent-contracts is an open-source toolkit that treats multi-agent workflows as 'contracts' rather than collections of prompts. Using a declarative YAML DSL, developers can precisely define: each Agent's identity and permissions, delegable tasks and rules, product ownership and status, validation rules, handover message structures, etc. Its contractual concept draws on the successful experience of OpenAPI in API governance, providing a specification layer for multi-agent systems.

Agent Contracts includes six DSL entities:

1. Agent: Defines the identity contract of the execution entity, including role, purpose, capabilities, permissions, etc.
2. Task: Defines the delegable work unit, specifying the target Agent, caller, input artifacts, etc.
3. Artifact: Defines the owner, state machine, and validation rules of circulating artifacts.
4. Tool: Defines the external capability interfaces that Agents can call.
5. Workflow: Defines the phased execution sequence, including steps, triggers, etc.
6. Guardrail: System-level cross-cutting constraints that support different execution strategies (block/warn/info).

Applicable Scenarios:

• Multi-Agent coding workflows (collaboration between architects, implementers, reviewers);
• Specification → implementation → audit → release pipeline;
• Internal Agent platforms (standardized contracts ensure cross-team consistency);
• High-audit-threshold fields (finance, healthcare, etc., with traceability and audit requirements).

Differences with Existing Frameworks:

Framework	Primary Focus	Agent Contracts' Differences
OpenAI Agents SDK	Runtime execution, tool calls, handovers	Focuses on design contracts, static guarantees, artifact relationships
CrewAI	Runtime task orchestration	More in-depth validation, ownership, inheritance, renderable design specifications
AutoGen	Code-first multi-Agent programming	More declarative, reviewable, CI-friendly

Other frameworks answer 'how to run Agents', while Agent Contracts answers 'what is the allowed structure of the system and how to maintain correctness'.

Usage Flow:

1. Define Contract: Declare system structure (e.g., Agent, Task, Artifact) in agent-contracts.yaml;
2. Validate Contract: Use agent-contracts validate to check syntax, references, and constraint consistency;
3. Render Prompts: Use agent-contracts render to convert contracts into Agent prompts, ensuring prompts align with the design.

Architectural Advantages:

• Explicitize implicit knowledge (convert rules in designers' minds into reviewable contracts);
• Support CI/CD integration (automatically validate design compliance on submission);
• Promote team collaboration (clarify responsibility boundaries and interaction rules);
• Evolvable design (versioned contracts support safe iteration).

Limitations:

• Not suitable for quickly building single-Agent chatbots or prompt prototypes;
• Does not solve runtime issues (scheduling, state management, fault recovery still require runtime frameworks).

Future Outlook: Agent Contracts represents systematic thinking about multi-agent system governance, introducing engineering methodologies by drawing on software engineering practices (interface contracts, static analysis). As AI Agents move toward production, such governance tools will become more important, and we look forward to more community innovations around Agent engineering.