6. Agentic AI Reference Architecture - stanlypoc/AIRA GitHub Wiki

Agentic AI Reference Architecture

1. Introduction

1.1 Purpose

Standardized framework for designing autonomous agent systems across capability levels (Assistant → Collaborative → Self-Evolving).

1.2 Audience

  • AI Architects
  • Agent System Developers
  • Compliance Officers
  • Enterprise IT Teams

1.3 Scope & Applicability

In Scope:

  • Goal-driven autonomous agents
  • Multi-agent coordination
  • Continuous learning systems

Out of Scope:

  • Non-agentic AI models
  • Hardware-specific robotics

1.4 Assumptions & Constraints

Prerequisites:

  • Python 3.10+
  • Graph database knowledge

Technical Constraints:

  • Minimum 8 vCPUs per agent
  • <500ms inter-agent latency

Ethical Boundaries:

  • No irreversible actions without human confirmation
  • Mandatory intention transparency

1.6 Example Models

Level Examples
Basic AutoGPT, BabyAGI
Advanced Microsoft AutoGen, CrewAI
Autonomous DeepMind SIMA, Adept ACT-1

2. Architectural Principles

Here are the Architecture Principles of Agentic AI, which are distinct from traditional AI systems due to their autonomous, goal-directed, and multi-step reasoning nature. These principles are essential for designing robust, trustworthy, and effective AI agents in enterprise or open-ended environments.

🤖 2.1 Architecture Principles for Agentic AI

1. Goal-Oriented Autonomy

Agents must operate with explicit goals, capable of breaking them into subtasks without constant human prompts.

  • Embed planning capabilities (e.g., task trees, chains of thought).
  • Use LLMs with memory and task-awareness (e.g., ReAct, AutoGPT).
  • Balance initiative with guardrails to prevent drift.

2. Modular Tool Usage

Agents should interact with tools via clear, standardized interfaces.

  • Use function calling (e.g., OpenAI Tools, LangChain Tools) or APIs.
  • Allow dynamic tool selection based on context or task requirements.
  • Track tool usage logs for audit and debugging.

3. Long-Term Memory & Context Management

Agents need episodic and semantic memory to perform multi-step reasoning.

  • Implement vector stores (e.g., FAISS, Chroma, Pinecone) for embedding-based memory.
  • Use sliding windows, RAG, or token-efficient memory for LLMs.
  • Support forgetting, memory scoring, and relevance filtering.

4. Task Decomposition and Planning

Enable agents to deconstruct complex objectives into manageable actions.

  • Support planning modules (e.g., PDDL, hierarchical planners).
  • Allow iterative re-planning based on outcomes or errors.
  • Track progress using task graphs or workflows.

5. Reflective Reasoning & Self-Critique

Agentic AI should include feedback loops to validate or revise decisions.

  • Leverage self-evaluation prompts, retries, or deliberation models.
  • Use ReAct-style loops: think → act → observe → revise.
  • Implement checkpoints to limit cascading failure.

6. Multi-Agent Collaboration

Enable teams of agents to cooperate, specialize, or negotiate.

  • Use orchestrators to manage roles, turn-taking, and messaging (e.g., AutoGen, CrewAI).
  • Support message protocols (JSON, LangChain Agent Protocol).
  • Monitor communication bandwidth, delegation, and agent “trust scores.”

7. Grounding in External Environments

Agents must interact with real systems via APIs, UIs, sensors, or simulations.

  • Provide API gateways, RPA bridges, or environment simulators.
  • Ensure grounding accuracy by validating perceptions or actions.
  • Map agent actions to real-world effects in business workflows.

8. Security and Containment

Agents must operate within clearly defined boundaries to avoid unsafe behavior.

  • Use sandboxing, API whitelisting, execution limits (time, memory).
  • Apply role-based access and rate limiting.
  • Require approval for high-impact actions (e.g., database writes, purchases).

9. Observability & Transparency

Make agent decisions traceable, inspectable, and debuggable.

  • Log every reasoning step, tool invocation, and response.
  • Visualize thought chains, memory access, and decision trees.
  • Provide human-in-the-loop override capabilities.

10. Ethics, Alignment, and Governance

Align agent behavior with human intent, values, and policies.

  • Use constitutional AI prompts, alignment tuning, or ethical reward signals.
  • Incorporate policy engines (e.g., OPA) to filter actions or plans.
  • Monitor for hallucinations, deception, or misaligned optimization.

🧭 Bonus: Agent Lifecycle Management

Think beyond runtime—agents should support:

  • Versioning: Tasks and skills evolve
  • Retention: Archived memory or experiences
  • Reusability: Skills shared across agents

2.2 Standards Compliance

  1. Security & Privacy

    • Must comply with: NIST AI RMF, ISO 27001 Annex A.14
    • Practical tip: Agent-to-agent TLS 1.3 encryption

  2. Ethical AI

    • Key standards: IEEE 7007 (Ontological Standard)
    • Checklist item: Action impact simulation before execution

2.3 Operational Mandates

5 Golden Rules:

  1. Maintain chain of custody for all actions
  2. Time-bound task execution (max 24h autonomy)
  3. Three-way consensus for critical decisions
  4. Real-time capability advertising
  5. Immutable audit trails

Sample Audit Log:

{
  "timestamp": "2023-11-20T14:23:12Z",
  "agent_id": "logistics-bot-7",
  "action": "route_optimization",
  "input_hash": "sha3-512:a1b2...",
  "authorizing_agents": ["safety-bot-3", "supervisor-bot-1"]
}

3. Architecture by Technology Level

3.1 Level 2 (Basic) - Single-Agent Assistants

Definition:
Goal-oriented agents with fixed capability sets.

Key Traits:

  • Single objective focus
  • Limited context window (≤8K tokens)
  • Pre-defined tool library

Logical Architecture:

graph LR
    A[User Request] --> B[Agent Core]
    B --> C[Tool Selector]
    C --> D[Action Executor]
    D --> E[Result Formatter]

Cloud Implementations:

Provider Services
Azure Autogen Studio + Cognitive Services
AWS Bedrock Agents + Lambda
GCP Vertex AI Agent Builder

3.2 Level 3 (Advanced) - Multi-Agent Collaboration

Definition:
Specialized agents with dynamic team formation.

Key Traits:

  • Role-based specialization
  • Negotiation protocols
  • Shared memory space

Logical Architecture:

graph LR
    A[Task] --> B[Orchestrator]
    B --> C[Specialist Agent 1]
    B --> D[Specialist Agent 2]
    C & D --> E[Consensus Engine]
    E --> F[Output]

Critical Components:

  • Agent directory service
  • Conflict resolution module
  • Capability marketplace

3.3 Level 4 (Autonomous) - Self-Evolving Systems

Definition:
Agents that modify their own architecture.

Key Traits:

  • Dynamic skill acquisition
  • Ethical constraint propagation
  • Self-modeling

Logical Architecture:

graph LR
    A[Environment] --> B[Meta-Cognition Layer]
    B --> C[Skill Generator]
    C --> D[Ethical Validator]
    D --> E[Deployed Agent]
    E -->|Feedback| B

Safety Mechanisms:

  • Constitutional AI layer
  • Red team simulation sandbox
  • Behavior cloning rollback

4. Glossary & References

Terminology:

  • Agentic Loop: Perception→Planning→Execution→Learning cycle
  • Stigmergy: Environment-mediated agent coordination

References:

  1. Agent Systems RFC
  2. NIST AI 100-2 (Agent Architectures)