Skip to main content

🔧 Agentic AI Blueprint

· 2 min read

1. Cognitive Core (Reasoning & Intelligence)​

  • LLM Connectors → access to foundational models (GPT, Claude, LLaMA, Mistral)
  • Planning Engines → task decomposition, tree search, Monte Carlo planning
  • Reasoning Frameworks → ReAct, chain-of-thought, graph-of-thought, self-reflection
  • Meta-Cognition → agents evaluating their own confidence/uncertainty

2. Memory & Knowledge Substrate​

  • Short-Term Memory → current context window
  • Long-Term Memory → vector DBs, knowledge graphs
  • Episodic Memory → logs of interactions, task histories
  • Semantic Memory → factual knowledge bases
  • Procedural Memory → how-tos, learned workflows
  • Associative Memory → linking related knowledge dynamically

3. Data & Perception​

  • Data Sources → internal documents, APIs, web, real-time feeds
  • Multimodal Perception → text, images, video, audio, sensor data
  • Data Fusion Layer → integrates multimodal inputs into coherent context
  • Knowledge Grounding → verifying facts against trusted sources

4. Agency & Action​

  • Agentic Connectors → APIs, tools, robotic systems
  • Execution Sandbox → safe environment for running code or simulations
  • Multi-Agent Protocols (MCPs) → collaboration, delegation, negotiation
  • Embodiment Layer → integration with hardware (IoT, robotics, AR/VR avatars)

5. Prompting & Expression​

  • Prompt Templates & Libraries → reusable scaffolds
  • Dynamic Prompt Assembly → injects context, goals, retrieved knowledge
  • Prompt Optimization Systems → few-shot, self-critique, retrieval-augmented prompting
  • Persona & Role Systems → define the "character" or style of the agent
  • Multi-Lingual/Narrative Engines → adapt tone, language, storytelling

6. Evaluation & Governance​

  • Eval Systems → correctness, coherence, bias, safety
  • Guardrails → content filters, alignment layers, access control
  • Observability & Logging → traceability for debugging and audits
  • Ethics & Compliance → regulatory alignment, explainability modules
  • Trust & Verification → cryptographic signatures, provenance tracking

7. Adaptation & Learning​

  • Feedback Loops → user ratings, implicit signals, reinforcement learning
  • Experimentation Layer → A/B testing, model selection, prompt trials
  • Self-Improvement → agents refine their own strategies
  • Meta-Learning Systems → generalize lessons across tasks

8. Interaction & Experience​

  • User Interface Layer → chat, dashboards, voice assistants, immersive XR
  • Contextual Personalization → user profiles, preferences, history
  • Collaboration Interfaces → multi-user shared agents, team AI assistants
  • Emotion & Empathy Modules → affective computing for human-like interaction

9. Infrastructure & Scale​

  • Compute Orchestration → model routing, hybrid cloud, GPU scheduling
  • Cost Optimization Systems → model caching, distillation, adaptive compression
  • Resilience & Failover → redundancy, fallback models, recovery systems
  • Security Layer → data privacy, adversarial defense, red-teaming

10. Futuristic / Experimental Components​

  • Causal Reasoning Engines → moving beyond correlation to cause-effect reasoning
  • Simulation Environments → agents run "mental models" before acting
  • Swarm Intelligence → emergent behavior from many small agents
  • Consciousness Probes (research only) → monitoring self-awareness signals
  • Ethical Governors → adaptive moral reasoning depending on context