Self-Aware Agent Architecture

The agent system is an adaptive AI architecture that autonomously generates evaluation criteria, produces multiple response candidates, and iteratively improves through confidence-based self-correction. The system leverages LLM-as-a-judge mechanisms combined with long-term agent memory to deliver high-quality outputs while continuously enhancing its own evaluation capabilities and prompt strategies.

A concurrent agentic evaluation process runs in the background of the chatbot and does not interfere with or degrade the user experience unless intervention is warranted. A meter is displayed to the user showing answer entropy and updates after every AI generated answer. The user can set a threshold for the level of entropy they deem acceptable for their research context. If the entropy crosses the threshold, an intervention event occurs where the agent will autonomously take corrective action. Corrective action could manifest as anything from retrieving additional context to asking the user for more information. Once an answer with acceptable entropy is achieved a query that required corrective action, the query/answer pair is persisted in long-term memory and will be used as a few-shot example for similar queries in the future.

Core Architecture Components

1. Response Generation Pipeline

• Agent creates 3-5 response candidates for each user prompt
• Each response includes explicit chain-of-thought reasoning
• Responses tagged with initial confidence estimates
• Parallel generation paths for diverse output exploration

2. LLM-as-Judge Meta-Evaluation System

• Dynamically creates evaluation criteria based on task context
• Multiple specialized judge models for different evaluation aspects
• Extracts confidence scores from model token probabilities
• Configurable confidence thresholds (e.g., >90% auto-accept, 50-90% human review, <50% auto-reject)

3. Agent Corrective Logic Engine

• Identifies failure modes and success patterns
• Creates new evaluation criteria based on historical performance
• Generates improved prompts for both response generation and evaluation
• Develops specialized evaluation tools for domain-specific tasks

4. Memory & Knowledge Management

• Episodic Memory: Stores interaction history and performance metrics
• Semantic Memory: Maintains knowledge about user preferences and successful patterns
• Vector Embeddings: MongoDB Atlas vector search for semantic similarity matching

Architecture Flow

User Input → Response Generator → Multiple Candidates → Judge Evaluation → 
Confidence Analysis → Threshold Decision → Output/Correction Loop → 
Memory Storage → Pattern Analysis → Criteria/Prompt Updates

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graph TD
  UserPrompt[User Prompt Input] --> ResponseGen[Response Generation Pipeline]
  
  ResponseGen --> Response1[Response 1<br/>• Confidence Score<br/>• Message Content]
  ResponseGen --> Response2[Response 2<br/>• Confidence Score<br/>• Message Content]
  ResponseGen --> Response3[Response 3<br/>• Confidence Score<br/>• Message Content]
  
  Response1 --> JudgeSystem[LLM-as-Judge<br/>Meta-Evaluation]
  Response2 --> JudgeSystem
  Response3 --> JudgeSystem
  
  JudgeSystem --> LogProbs[Log Probabilities<br/>Confidence Analysis]
  LogProbs --> ConfidenceAvg[Confidence Average<br/>Calculation]
  
  ConfidenceAvg --> Threshold{Confidence<br/>Threshold<br/>Analysis}
  
  Threshold -->|>90% Auto-Accept| Output[Final Output<br/>Delivery]
  Threshold -->|50-90% Review| HumanReview[Human Review<br/>Required]
  Threshold -->|<50% Reject| AgentCorrection[Agent Corrective<br/>Logic Engine]
  
  AgentCorrection --> CriteriaGen[Dynamic Criteria<br/>Generation]
  AgentCorrection --> PromptOpt[Prompt Strategy<br/>Optimization]
  AgentCorrection --> ToolCreation[Specialized Tool<br/>Development]
  
  CriteriaGen --> MemoryStore[Memory & Knowledge<br/>Management System]
  PromptOpt --> MemoryStore
  ToolCreation --> MemoryStore
  
  MemoryStore --> EpisodicMem[Episodic Memory<br/>Interaction History]
  MemoryStore --> SemanticMem[Semantic Memory<br/>User Preferences]
  MemoryStore --> VectorStore[Vector Embeddings<br/>MongoDB Atlas]
  
  HumanReview --> Feedback[User Feedback<br/>Upvote/Downvote]
  Output --> Feedback
  
  Feedback --> MemoryStore
  
  style UserPrompt fill:#e1f5fe
  style ResponseGen fill:#f3e5f5
  style JudgeSystem fill:#fff3e0
  style Threshold fill:#f1f8e9
  style AgentCorrection fill:#ffebee
  style MemoryStore fill:#f9fbe7

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Technical Stack

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graph LR
  subgraph "Frontend Layer"
    UI[Real-time Confidence Display]
  end
  
  subgraph "API Layer"
    FastAPI[FastAPI Server<br/>• Async Processing]
  end
  
  subgraph "Orchestration Layer"
    LangGraph[LangGraph<br/>• State Machine Workflow<br/>• Agent Coordination<br/>]
    LangMem[LangMem<br/>• Long-term Memory<br/>• Cross-session Context<br/>• Knowledge Persistence]
  end
  
  subgraph "ML Layer"
    PrimaryLLM[GPT-4/Claude<br/>• Response Generation<br/>• Multi-candidate Output]
    JudgeLLM[Judge LLM<br/>• Quality Evaluation<br/>• Confidence Scoring<br/>• Meta-assessment]
    Embeddings[Text Embeddings<br/>• Semantic Similarity<br/>• Vector Representations]
  end
  
  subgraph "Storage Layer"
    MongoDB[MongoDB Atlas<br/>• Vector Search<br/>• Semantic Indexing]
  end
  
  UI --> FastAPI
  FastAPI --> LangGraph
  LangGraph --> LangMem
  LangGraph --> PrimaryLLM
  LangGraph --> JudgeLLM
  LangMem --> MongoDB
  Embeddings --> MongoDB
  
  style UI fill:#e3f2fd
  style FastAPI fill:#e8f5e8
  style LangGraph fill:#fff3e0
  style LangMem fill:#f3e5f5
  style PrimaryLLM fill:#ffebee
  style JudgeLLM fill:#f1f8e9
  style MongoDB fill:#e1f5fe

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LangGraph Nodes

• GPT-4/Claude for response generation
• Separate model instance for evaluation tasks
• Log probability extraction and normalization
• Text embedding models for semantic search

Performance KPIs

• Response quality scores (human evaluation baseline)
• Confidence calibration accuracy (predicted vs. actual quality)
• Self-improvement rate (performance gains over time)
• User satisfaction ratings

Technical Metrics

• Response latency (<2s for generation, <1s for evaluation)
• Confidence score accuracy (±10% of human assessment)
• Memory retrieval speed (<100ms for semantic search)
• System uptime and reliability (99.9% target)

Quality Assurance

• Human oversight for low-confidence decisions
• Regular evaluation of judge performance
• Fallback mechanisms for system failures
• Audit trails for all decisions and improvements