Product Requirements Document (PRD): AI Agent Orchestration Platform (v1.0 - Core & Future Expansion)

1. Introduction

This document defines the requirements for the AI Agent Orchestration Platform, starting with the initial core version (v1.0) and outlining future expansion areas. The platform enables users to visually design, execute, monitor, and manage workflows composed of various AI agents, incorporating Human-in-the-Loop (HITL) steps. The initial version focuses on providing the foundational capabilities for a software developer/solopreneur user, while future expansions will address multi-modal agents, edge computing, federated collaboration, and AI-driven self-optimization. The platform is designed for extensibility, interoperability (via open standards like A2A), and a vibrant agent ecosystem/marketplace across industries and deployment environments.

2. Goals

2.1 Goals for v1.0 (Core)

Establish the core orchestration engine and backend infrastructure.
Implement a functional visual workflow builder supporting key agent types and basic control flow.
Provide essential tracking and monitoring capabilities for workflow runs.
Integrate a robust HITL mechanism (multi-step, escalation, comms integration).
Support execution of agents via Docker containers, API calls, and emerging protocols (A2A/Open Agent Protocol).
Integrate foundational LLMOps and system observability (Langfuse, Trulens, Arize, PromptLayer, OpenTelemetry).
Lay the groundwork for a public/private agent registry and marketplace.
Support SaaS/multi-tenancy and enterprise-grade security/compliance from the start.

2.2 Goals for Future Expansion

Enable seamless integration of text, vision, audio, sensor, and robotics agents within unified workflows.
Provide specialized visualization and monitoring tools for multi-modal agent outputs.
Support AR/VR interfaces for immersive workflow design and monitoring.

Edge Computing Capabilities

Support deployment and execution of agent workflows from cloud to edge with offline capabilities.
Optimize for resource-constrained environments with lightweight runtimes.
Enable mesh networking for agent collaboration across distributed edge nodes.

Federated Collaboration

Facilitate secure cross-organization workflows with privacy-preserving computation.
Implement federated learning framework for distributed model training.
Support zero-knowledge proofs for verification without revealing sensitive data.

AI-Driven Self-Optimization

Leverage AI to continuously improve workflow efficiency, resource utilization, and fault tolerance.
Implement anomaly detection and predictive scaling for proactive management.
Develop self-healing capabilities for automated recovery from failures.

Advanced Marketplace Ecosystem

Build comprehensive tools for agent monetization, quality assurance, and community governance.
Implement industry-specific compliance modules for healthcare, finance, etc.
Create advanced developer tools and SDKs for building marketplace-ready agents.

3. User Personas

3.1 Core Platform Personas (v1.0)

Alex (Primary): A software developer building AI agents for client projects and personal automation. Needs to quickly define, test, run, and debug workflows involving different agent types (containers, APIs, A2A, etc.). Needs HITL for client approvals and personal choices. Values observability, security, and extensibility.

Sam (Secondary): A solopreneur wanting to automate personal and business tasks using prebuilt agents. Requires an intuitive interface for workflow design and execution.

Jordan (Tertiary): An enterprise architect requiring compliance, observability, and multi-tenancy for large-scale deployments.

3.2 Expanded Scope Personas

Dr. Maya: A healthcare researcher using multi-modal agents to process medical imaging, patient data, and sensor readings. Requires HIPAA compliance, federated learning across institutions, and specialized visualization tools.

Carlos: A manufacturing engineer deploying AI workflows to edge devices in a smart factory. Needs offline operation, resource optimization, and integration with robotics and IoT sensors.

Priya: A financial analyst building cross-organization workflows with strict privacy requirements. Needs secure multi-party computation, audit trails, and PCI-DSS compliance.

Raj: An AR/VR developer creating immersive experiences powered by AI agents. Requires real-time agent orchestration, specialized visualization, and integration with AR/VR frameworks.

Elena: A marketplace creator building and monetizing specialized agents. Needs comprehensive tools for quality assurance, revenue sharing, and community engagement.

4. Functional Requirements

4.1. Workflow Design (Visual Builder)

REQ-WD-001: Users shall be able to create, save, and load workflow definitions via a visual, node-based interface (React Flow).
REQ-WD-002: The interface shall provide a palette of nodes including:
Start / End nodes.
Agent Task: Docker Container Runner node.
Agent Task: API Caller node (configurable HTTP method, URL, headers, body).
Agent Task: A2A/Open Agent Protocol node (for cross-platform agent interoperability).
HITL: Human Approval node (multi-step, escalation, comms integration).
Basic Control Flow: Conditional Branching (based on output of a previous node).
REQ-WD-003: Users shall be able to draw directed edges between nodes to define execution dependencies.
REQ-WD-004: Users shall be able to select a node and configure its parameters in a dedicated panel.
Docker Node: Image name, command, input environment variables/volume mounts, output extraction method.
API Node: URL, method, headers (support for secret refs), request body template, response parsing config.
A2A Node: Protocol, agent endpoint, supported actions, data contracts.
Approval Node: Assignee (user/role placeholder), instructions, approval/rejection outcomes, escalation path, comms integration.
Branch Node: Condition logic based on upstream node output (e.g., output.status == 'success').
REQ-WD-005: Users shall be able to define input parameters for the entire workflow.
REQ-WD-006: The system shall perform basic validation on the workflow graph before saving (e.g., detect unconnected nodes).
REQ-WD-007: Real-time collaborative editing and AI-assisted workflow suggestions.

4.2. Workflow Execution & Orchestration

REQ-EXE-001: Users shall be able to manually trigger a workflow run from the UI, providing necessary input parameters.
REQ-EXE-002: The backend shall translate the visual workflow definition into an executable format for the chosen orchestrator (Temporal or Prefect preferred).
REQ-EXE-003: The orchestrator shall execute tasks based on the defined dependencies.
REQ-EXE-004: The orchestrator shall execute Docker container agents via the configured Docker runtime/Kubernetes.
REQ-EXE-005: The orchestrator shall execute API call agents by making HTTP requests.
REQ-EXE-006: The orchestrator shall execute A2A protocol agents for cross-platform interoperability.
REQ-EXE-007: The orchestrator shall pause workflow execution when an HITL node is reached and await external input/trigger, with support for escalation and comms integration.
REQ-EXE-008: The orchestrator shall handle basic task retries on failure based on node configuration.
REQ-EXE-009: The orchestrator shall manage the state of workflow runs (Running, Succeeded, Failed, Paused).
REQ-EXE-010: The orchestrator shall support multi-tenant execution and namespace isolation.

4.3. Workflow Tracking & Monitoring

REQ-MON-001: Users shall be able to view a list of historical and active workflow runs.
REQ-MON-002: For each run, users shall be able to view its overall status, start/end times, and duration.
REQ-MON-003: Users shall be able to view a detailed run page showing the visual workflow graph with real-time node status indicators (queued, running, succeeded, failed, paused).
REQ-MON-004: Users shall be able to select a completed or failed task instance and view its inputs, outputs, and execution logs (stdout/stderr for containers, API request/response details).
REQ-MON-005: Advanced integration with LLMOps and observability tools (Langfuse, Trulens, Arize, PromptLayer, Grafana, Prometheus, Loki, OpenTelemetry) for prompt/version tracking, tracing, and feedback loops.

4.4. Human-in-the-Loop (HITL)

REQ-HITL-001: Users shall be able to view a list of HITL tasks assigned to them ("My Tasks").
REQ-HITL-002: Users shall be able to open an HITL task and view the context (instructions, data from previous step).
REQ-HITL-003: Users shall be able to submit a decision (e.g., Approve/Reject) for an Approval task, with support for multi-step reviews and escalation.
REQ-HITL-004: Upon submission, the backend shall trigger the resumption of the corresponding workflow execution in the orchestrator.
REQ-HITL-005: HITL tasks can be integrated with external communication tools (Slack, email).

4.5. Agent Registry, Marketplace & Ecosystem

REQ-REG-001: Users shall be able to register, manage, and reuse agent configurations securely.
REQ-REG-002: The platform shall support a public/private marketplace for sharing and discovering agents, templates, and plugins.
REQ-REG-003: Support for community-driven contributions and extensibility.
REQ-REG-004: The marketplace shall provide comprehensive monetization tools including payment processing, subscription management, and revenue sharing.
REQ-REG-005: The platform shall implement a quality assurance pipeline for automated testing, compliance verification, and security scanning of marketplace items.
REQ-REG-006: The marketplace shall support a community governance framework for decentralized management of policies and standards.

4.6. Platform & Infrastructure

REQ-PLAT-001: The system shall provide secure user authentication (SSO, OIDC, SAML).
REQ-PLAT-002: The system shall use a relational database (PostgreSQL) for storing core data, with support for edge-compatible storage and federated data sharing.
REQ-PLAT-003: The system shall provide a mechanism for securely managing credentials/secrets used by agents (integration with Vault or similar).
REQ-PLAT-004: Support for SaaS/multi-tenancy, namespaces, and workspace isolation.
REQ-PLAT-005: Support for audit logging, compliance (GDPR, SOC2, HIPAA, PCI-DSS), and zero-trust execution.
REQ-PLAT-006: The system shall implement secure multi-party computation for privacy-preserving collaboration.
REQ-PLAT-007: Support for homomorphic encryption and zero-knowledge proofs for secure data processing and verification.
REQ-PLAT-008: The system shall provide industry-specific compliance modules for healthcare, finance, and other regulated industries.

REQ-MM-001: The platform shall support vision agents for processing image and video data.
REQ-MM-002: The platform shall support audio agents for processing speech and sound data.
REQ-MM-003: The platform shall support sensor data agents for processing IoT and device telemetry.
REQ-MM-004: The platform shall provide specialized visualization tools for multi-modal agent outputs.
REQ-MM-005: The platform shall support AR/VR interfaces for immersive workflow design and monitoring.
REQ-MM-006: The system shall enable integration with robotics frameworks like ROS (Robot Operating System).

4.8. Edge Computing Capabilities

REQ-EDGE-001: The platform shall support deployment of workflows to edge devices with resource constraints.
REQ-EDGE-002: The system shall provide offline operation capabilities with local storage and synchronization.
REQ-EDGE-003: The platform shall optimize resource utilization for constrained environments.
REQ-EDGE-004: The system shall support mesh networking for agent collaboration across distributed nodes.
REQ-EDGE-005: The platform shall provide lightweight telemetry for edge devices with offline buffering.

4.9. Federated Collaboration

REQ-FED-001: The platform shall enable secure cross-organization workflows with access controls.
REQ-FED-002: The system shall implement a federated learning framework for distributed model training.
REQ-FED-003: The platform shall support zero-knowledge proofs for verification without revealing sensitive data.
REQ-FED-004: The system shall provide secure data sharing mechanisms between organizations.
REQ-FED-005: The platform shall maintain audit trails for all cross-organization interactions.

4.10. AI-Driven Self-Optimization

REQ-AI-001: The platform shall implement AI-driven workflow optimization for improved efficiency.
REQ-AI-002: The system shall provide anomaly detection for identifying unusual agent behavior.
REQ-AI-003: The platform shall support predictive scaling based on historical patterns.
REQ-AI-004: The system shall implement self-healing capabilities for automated recovery from failures.
REQ-AI-005: The platform shall provide AI-assisted debugging tools for troubleshooting workflow issues.

5. Acceptance Criteria

5.1 Core Platform Acceptance Criteria

Each workflow can be saved, loaded, and executed end-to-end.
Agent outputs and logs are visible in the UI.
HITL steps pause execution and resume on approval.
Marketplace allows agent/template discovery and reuse.

5.2 Expanded Scope Acceptance Criteria

Multi-modal agents successfully process and visualize different data types (vision, audio, sensor).
Workflows deploy and execute on edge devices with offline capabilities.
Cross-organization workflows maintain data privacy while achieving collaborative goals.
AI-driven optimizations measurably improve workflow efficiency and reliability.
Marketplace monetization tools successfully process payments and distribute revenue.

6. Non-Functional Requirements

6.1 Core Platform Non-Functional Requirements (v1.0)

NFR-001 (Usability): The visual builder interface should be intuitive for users familiar with node-based tools, with AI-driven UX enhancements.
NFR-002 (Reliability): The orchestration engine should reliably execute workflows and handle basic task failures with retries.
NFR-003 (Observability): Core execution logs must be accessible for debugging failed runs. Deep integration with LLMOps tools for tracing and analytics.
NFR-004 (Security): Basic authentication and secure credential handling must be implemented. Support for enterprise SSO and compliance standards.
NFR-005 (Extensibility): The platform should be extensible via plugins, marketplace, and open APIs.
NFR-006 (Scalability): Multi-tenant architecture should support scaling to hundreds of concurrent workflows and thousands of agent executions.

6.2 Expanded Scope Non-Functional Requirements

NFR-007 (Multi-Modal Support): The platform should seamlessly handle diverse data types including text, images, audio, video, and sensor data with appropriate visualization tools.
NFR-008 (Edge Performance): Edge deployments should operate efficiently on devices with limited resources (CPU, memory, network) and support offline operation.
NFR-009 (Federated Privacy): Cross-organization workflows must maintain data privacy and comply with relevant regulations while enabling effective collaboration.
NFR-010 (Self-Optimization): AI-driven optimizations should measurably improve workflow efficiency, resource utilization, and fault tolerance over time.
NFR-011 (Marketplace Quality): The marketplace should maintain high-quality standards through automated testing, compliance verification, and community governance.
NFR-012 (Adaptive UX): User interfaces should adapt to different skill levels, roles, and preferences, with support for multi-modal interaction.
NFR-013 (Compliance): The platform should meet industry-specific compliance requirements including HIPAA for healthcare and PCI-DSS for financial applications.

7. Implementation Phases

7.1 Out-of-Scope for v1.0 (Core Platform)

Advanced control flow (loops, parallel execution).
Support for more agent types (Scripts, Cloudflare, specific frameworks like CrewAI/Autogen adapters, advanced A2A features).
Workflow scheduling and event-based triggers (webhooks).
Agent Registry and Marketplace expansion.
Advanced monitoring dashboards within the platform UI.
Role-based access control.
Workflow versioning.
Detailed cost tracking.
Vector DB integration.
Real-time collaborative editing and co-authoring.
AI-driven workflow auto-completion and diagnostics.

7.2 Implementation Roadmap for Expanded Scope

Phase 1: Core Platform (v1.0)

Establish the foundation with core orchestration, visual builder, HITL, and basic observability.
Focus on containerized, API-based, and A2A protocol agents.
Implement essential security, compliance, and marketplace features.

Add support for vision, audio, and sensor data agents.
Implement edge deployment framework and offline operation capabilities.
Develop initial AR/VR and robotics integration.

Phase 3: Enterprise & Federated (v3.0)

Enhance enterprise security with industry-specific compliance modules.
Implement federated learning framework and secure multi-party computation.
Develop advanced audit and forensics capabilities.

Phase 4: Self-Optimizing Platform (v4.0)

Integrate AI-driven workflow optimization, predictive scaling, and anomaly detection.
Implement self-healing capabilities and advanced debugging tools.
Develop performance analytics and resource optimization.

Phase 5: Advanced Ecosystem (v5.0)

Develop comprehensive monetization framework and quality assurance program.
Implement community governance model for the marketplace.
Create advanced developer tools and SDKs for building marketplace-ready agents.

8. Conclusion

This PRD outlines the requirements for the AI Agent Orchestration Platform, starting with a functional core platform (v1.0) that allows users to visually build, run, monitor, and debug workflows involving containerized, API-based, and interoperable (A2A) agents with robust HITL, observability, extensibility, and compliance. The expanded scope includes multi-modal agent support, edge computing capabilities, federated collaboration, AI-driven self-optimization, and an advanced marketplace ecosystem, positioning the platform as the definitive solution for AI agent orchestration across industries, modalities, and deployment environments.