# AI Agents and Conversational AI Processes Backlog

This backlog contains processes, methodologies, work patterns, and flows for the AI Agents and Conversational AI specialization.

## Process Categories

### Agent Architecture Implementation

- [ ] **ReAct Agent Implementation**
  - Description: Process for implementing a ReAct (Reasoning and Acting) agent that interleaves thought-action-observation loops for multi-step reasoning tasks with explicit reasoning traces
  - References: ReAct paper, LangChain ReAct agent, agent frameworks
  - Tools: LangChain, LlamaIndex, OpenAI function calling, Claude tool use
  - Outputs: ReAct agent implementation, reasoning traces, tool integration, evaluation metrics

- [ ] **Plan-and-Execute Agent Development**
  - Description: Process for building hierarchical agents that separate high-level planning from low-level execution with replanning capabilities based on intermediate results
  - References: Plan-and-Execute pattern, hierarchical task decomposition
  - Tools: LangGraph, LangChain, CrewAI
  - Outputs: Planner module, executor module, replanning logic, monitoring dashboard

- [ ] **Multi-Agent System Design and Implementation**
  - Description: Process for designing and implementing multi-agent systems with coordination patterns (hierarchical, cooperative, competitive, sequential, parallel) and agent communication protocols
  - References: Multi-agent patterns, CrewAI, agent swarms
  - Tools: CrewAI, LangGraph, AutoGen, multi-agent frameworks
  - Outputs: Agent roles, coordination logic, communication protocol, system architecture

- [ ] **LangGraph Workflow Design**
  - Description: Process for building stateful multi-agent workflows using LangGraph with cyclic graphs, state management, checkpoints, and human-in-the-loop integration points
  - References: LangGraph documentation, stateful workflows
  - Tools: LangGraph, LangSmith, state management patterns
  - Outputs: Workflow graph definition, state schema, checkpointing logic, integration points

### Conversational AI Development

- [ ] **Chatbot Design and Implementation**
  - Description: Comprehensive process for designing and implementing chatbots including conversation flow design, intent/entity recognition, dialogue management, and multi-channel deployment
  - References: Conversation design patterns, chatbot frameworks
  - Tools: Rasa, Botpress, LangChain, Dialogflow, custom implementations
  - Outputs: Conversation flows, NLU models, dialogue policy, deployed chatbot

- [ ] **Dialogue Flow and Conversation Design**
  - Description: Process for designing natural conversation flows including happy paths, error handling, clarification strategies, context switching, and conversation repair mechanisms
  - References: Conversation design principles, UX for conversational interfaces
  - Tools: Voiceflow, flowchart tools, conversation design platforms
  - Outputs: Dialogue flow diagrams, conversation scripts, error handling strategies, UX guidelines

- [ ] **Intent Classification System Development**
  - Description: Process for building robust intent classification systems including training data creation, model selection, few-shot/zero-shot approaches, and confidence threshold tuning
  - References: Intent recognition techniques, BERT fine-tuning, LLM-based classification
  - Tools: Hugging Face Transformers, SetFit, OpenAI/Claude APIs, Rasa NLU
  - Outputs: Intent classification model, training dataset, evaluation metrics, API endpoints

- [ ] **Entity Extraction and Slot Filling Implementation**
  - Description: Process for implementing entity extraction systems with custom entity types, slot filling for dialogue state, entity linking, normalization, and composite entity handling
  - References: NER techniques, slot filling patterns, dialogue state tracking
  - Tools: spaCy, Hugging Face NER models, custom extractors
  - Outputs: Entity extraction models, slot filling logic, entity normalization rules

### Tool Use and Function Calling

- [ ] **Function Calling Agent with Tool Integration**
  - Description: Process for building agents with function calling capabilities including tool definition, input validation, parallel execution, error handling, and result aggregation
  - References: OpenAI function calling, Claude tool use, LangChain tools
  - Tools: OpenAI API, Anthropic API, LangChain Tools, Semantic Kernel
  - Outputs: Tool definitions, function calling logic, error handlers, integration tests

- [ ] **Custom Tool Development for Agents**
  - Description: Process for creating custom tools and functions for AI agents including API integrations, database queries, code execution sandboxes, and web browsing capabilities
  - References: Tool design patterns, security best practices, sandboxing
  - Tools: LangChain Tool framework, custom implementations, API clients
  - Outputs: Tool implementations, API wrappers, security controls, documentation

- [ ] **Tool Use Safety and Validation Framework**
  - Description: Process for implementing safety controls for tool-using agents including input validation, authentication, rate limiting, audit logging, and sandboxed execution
  - References: Security best practices, tool safety guidelines
  - Tools: Guardrails, validation libraries, audit logging systems
  - Outputs: Validation framework, security controls, audit logs, safety documentation

### RAG (Retrieval-Augmented Generation)

- [ ] **RAG Pipeline Design and Implementation**
  - Description: Comprehensive process for building RAG pipelines including document ingestion, chunking strategies, embedding generation, vector storage, retrieval, and generation
  - References: RAG patterns, LlamaIndex, LangChain retrievers
  - Tools: LlamaIndex, LangChain, vector databases (Pinecone, Weaviate, Chroma)
  - Outputs: RAG pipeline, indexing system, retrieval logic, evaluation metrics

- [ ] **Advanced RAG Pattern Implementation**
  - Description: Process for implementing advanced RAG patterns including hierarchical retrieval, multi-query RAG, agentic RAG, hybrid search, and self-RAG with quality assessment
  - References: Advanced RAG techniques, HyDE, Self-RAG papers
  - Tools: LlamaIndex advanced patterns, custom implementations
  - Outputs: Advanced retrieval strategies, query optimization, reranking logic, quality metrics

- [ ] **Vector Database Setup and Optimization**
  - Description: Process for selecting, configuring, and optimizing vector databases for semantic search including indexing strategies, metadata filtering, and performance tuning
  - References: Vector database comparisons, indexing strategies
  - Tools: Pinecone, Weaviate, Chroma, Qdrant, Milvus
  - Outputs: Vector DB configuration, indexing pipeline, query optimization, performance benchmarks

- [ ] **Chunking Strategy Design and Testing**
  - Description: Process for designing and testing document chunking strategies including fixed-size, semantic, recursive splitting, and small-to-big retrieval patterns
  - References: Chunking best practices, LlamaIndex node parsers
  - Tools: LangChain text splitters, LlamaIndex node parsers, custom implementations
  - Outputs: Chunking implementation, evaluation metrics, optimal parameters

### Memory Systems

- [ ] **Conversational Memory System Implementation**
  - Description: Process for implementing memory systems for conversational agents including short-term (conversation buffer), long-term (persistent storage), and semantic memory (vector-based retrieval)
  - References: LangChain memory patterns, memory architectures
  - Tools: LangChain Memory, Zep, MemGPT, vector databases
  - Outputs: Memory system implementation, storage backends, retrieval logic

- [ ] **Long-Term Memory and User Profile Management**
  - Description: Process for building persistent memory systems that track user preferences, historical interactions, learned facts, and personalization across sessions
  - References: User profiling, personalization patterns
  - Tools: Zep, Mem0, vector databases, traditional databases
  - Outputs: User profile system, fact extraction logic, cross-session persistence

### Prompt Engineering and Optimization

- [ ] **Prompt Engineering and Optimization Workflow**
  - Description: Systematic process for designing, testing, and optimizing prompts including few-shot examples, chain-of-thought, self-consistency, and constitutional AI principles
  - References: Prompt engineering techniques, LangSmith prompts
  - Tools: LangSmith, PromptLayer, Humanloop, A/B testing frameworks
  - Outputs: Optimized prompts, prompt templates, performance metrics, version control

- [ ] **System Prompt Design and Guardrails**
  - Description: Process for crafting effective system prompts with role definition, task boundaries, safety guidelines, and guardrails to prevent prompt injection and ensure aligned behavior
  - References: System prompt patterns, prompt injection defense, constitutional AI
  - Tools: Guardrails AI, NeMo Guardrails, custom validation
  - Outputs: System prompts, safety guidelines, injection detection, validation rules

### Safety and Alignment

- [ ] **Content Moderation and Safety Filters**
  - Description: Process for implementing content filtering for both inputs and outputs including toxicity detection, PII redaction, hallucination detection, and abuse prevention
  - References: Safety best practices, content moderation APIs
  - Tools: OpenAI Moderation API, Perspective API, LlamaGuard, Azure Content Safety
  - Outputs: Content filters, moderation pipeline, alert system, audit logs

- [ ] **Prompt Injection Detection and Defense**
  - Description: Process for implementing defenses against prompt injection attacks including input sanitization, instruction hierarchy, output validation, and LLM-based detection
  - References: Prompt injection defense strategies, security patterns
  - Tools: Rebuff, Guardrails AI, custom detection models
  - Outputs: Injection detection system, sanitization logic, validation rules, security documentation

- [ ] **Bias Detection and Fairness Audit**
  - Description: Process for auditing AI agents and chatbots for bias, implementing fairness testing, diversity in training data, and establishing monitoring and correction mechanisms
  - References: AI fairness frameworks, bias detection methodologies
  - Tools: Fairness indicators, counterfactual testing, manual audits
  - Outputs: Bias audit report, fairness metrics, mitigation strategies, monitoring dashboard

### Evaluation and Testing

- [ ] **Agent Evaluation Framework Implementation**
  - Description: Comprehensive process for evaluating agent performance including success metrics, task completion rates, reasoning quality, tool use accuracy, and LLM-as-judge evaluation
  - References: Agent benchmarks (AgentBench, WebArena), evaluation frameworks
  - Tools: LangSmith evaluation, custom evaluation scripts, LLM-as-judge
  - Outputs: Evaluation framework, test suites, metrics dashboard, benchmark results

- [ ] **Conversation Quality Testing and Metrics**
  - Description: Process for measuring conversational AI quality including intent accuracy, dialogue success rate, user satisfaction (CSAT), response appropriateness, and conversation coherence
  - References: Conversation metrics, dialogue evaluation
  - Tools: Custom metrics, user surveys, automated evaluation, analytics platforms
  - Outputs: Quality metrics, test conversations, user feedback analysis, improvement recommendations

- [ ] **A/B Testing for Conversational AI**
  - Description: Process for running A/B tests to compare different prompts, models, conversation flows, or agent architectures with statistical significance testing
  - References: A/B testing methodologies, statistical testing
  - Tools: A/B testing platforms, analytics tools, statistical libraries
  - Outputs: Experiment design, test results, statistical analysis, winning variants

- [ ] **Regression Testing for Agent Behavior**
  - Description: Process for implementing regression testing to ensure agent behavior remains consistent across updates including test case management and automated validation
  - References: Software testing best practices, CI/CD for agents
  - Tools: pytest, unit testing frameworks, CI/CD pipelines
  - Outputs: Test suites, regression tests, CI/CD integration, test reports

### Deployment and Operations

- [ ] **Agent Deployment Pipeline Setup**
  - Description: Process for deploying AI agents to production including containerization, API endpoint setup, scaling configuration, and multi-environment deployment
  - References: MLOps best practices, deployment patterns
  - Tools: Docker, Kubernetes, serverless platforms (Lambda, Cloud Run), FastAPI, LangServe
  - Outputs: Deployment pipeline, container images, API endpoints, scaling configuration

- [ ] **LLM Observability and Monitoring Implementation**
  - Description: Process for implementing comprehensive observability including request tracing, token usage tracking, error monitoring, conversation logging, and performance metrics
  - References: LLM observability best practices, tracing patterns
  - Tools: LangSmith, LangFuse, Phoenix (Arize), OpenTelemetry, custom logging
  - Outputs: Observability dashboard, tracing setup, alerting rules, log aggregation

- [ ] **Cost Optimization for LLM Applications**
  - Description: Process for optimizing costs in LLM applications including prompt compression, model selection, caching strategies, batch processing, and token budget management
  - References: Cost optimization strategies, efficiency techniques
  - Tools: LiteLLM, caching layers, prompt optimization tools
  - Outputs: Cost reduction strategies, caching implementation, optimized prompts, cost dashboard

- [ ] **Agent Performance Optimization**
  - Description: Process for optimizing agent latency and throughput including streaming responses, parallel execution, caching, model selection, and speculative execution
  - References: Performance optimization patterns, latency reduction techniques
  - Tools: Streaming APIs, parallel execution frameworks, caching systems
  - Outputs: Optimized agent implementation, latency benchmarks, throughput metrics

### Voice and Multi-Modal

- [ ] **Voice-Enabled Conversational AI Development**
  - Description: Process for building voice-enabled conversational interfaces including speech-to-text integration, text-to-speech synthesis, voice activity detection, and conversation management
  - References: Voice AI patterns, conversational voice design
  - Tools: Whisper, ElevenLabs, Google Speech APIs, Azure Speech, Amazon Transcribe/Polly
  - Outputs: Voice-enabled chatbot, ASR/TTS integration, conversation flow, audio processing pipeline

- [ ] **Multi-Modal Agent Implementation**
  - Description: Process for building agents that can process and generate multiple modalities (text, images, audio, video) using multi-modal LLMs and vision-language models
  - References: Multi-modal AI patterns, vision-language models
  - Tools: GPT-4 Vision, Claude 3, Gemini, LLaVA, BLIP-2
  - Outputs: Multi-modal agent, image processing, vision-language integration, evaluation metrics

### Fine-Tuning and Customization

- [ ] **LLM Fine-Tuning for Conversational AI**
  - Description: Process for fine-tuning language models for specific conversational tasks including data collection, training pipeline, evaluation, and deployment of custom models
  - References: Fine-tuning best practices, PEFT methods (LoRA, QLoRA)
  - Tools: Hugging Face Transformers, OpenAI fine-tuning, Axolotl, DeepSpeed
  - Outputs: Fine-tuned model, training pipeline, evaluation results, deployment configuration

- [ ] **Synthetic Conversation Data Generation**
  - Description: Process for generating high-quality synthetic training data for conversational AI using LLMs including diverse scenarios, edge cases, and multi-turn dialogues
  - References: Self-Instruct, Evol-Instruct, synthetic data generation techniques
  - Tools: GPT-4, Claude, data generation scripts, quality validation
  - Outputs: Synthetic conversation dataset, data validation pipeline, quality metrics

### Persona and Personality

- [ ] **Conversational Persona Design and Implementation**
  - Description: Process for designing and implementing consistent conversational personas including character traits, tone adaptation, brand voice alignment, and personality testing
  - References: Persona design frameworks, conversation design principles
  - Tools: Prompt engineering, personality guidelines, testing frameworks
  - Outputs: Persona guidelines, system prompts, tone examples, consistency tests

- [ ] **Empathetic Response Generation**
  - Description: Process for building conversational AI with empathetic response capabilities including sentiment detection, emotion recognition, and appropriate emotional responses
  - References: Empathetic dialogue research, emotional intelligence in AI
  - Tools: Sentiment analysis models, emotion classifiers, empathetic response templates
  - Outputs: Empathy module, sentiment detection, response adaptation logic

### Agent-Specific Patterns

- [ ] **Self-Reflection and Self-Correction Agent**
  - Description: Process for implementing agents with self-reflection capabilities that can critique their own outputs, detect errors, and self-correct through iterative refinement
  - References: Reflexion framework, self-critique patterns, constitutional AI
  - Tools: LangChain reflection patterns, multi-step reasoning
  - Outputs: Self-reflection logic, critique prompts, correction loops, quality improvements

- [ ] **Autonomous Task Planning and Execution**
  - Description: Process for building autonomous agents that can break down high-level goals into subtasks, plan execution strategies, and adaptively execute tasks
  - References: AutoGPT, BabyAGI, autonomous agent patterns
  - Tools: AutoGPT, BabyAGI, LangGraph, custom planning logic
  - Outputs: Task planning module, execution engine, goal management, autonomy safeguards

- [ ] **Knowledge Base Integration and Question Answering**
  - Description: Process for integrating knowledge bases into conversational AI for accurate question answering including semantic search, fact verification, and source citation
  - References: RAG patterns, question answering systems
  - Tools: Vector databases, knowledge graphs, RAG frameworks
  - Outputs: QA system, knowledge integration, citation system, accuracy metrics

## Summary

**Total Processes Identified: 40**

**Categories:**
- Agent Architecture Implementation (4 processes)
- Conversational AI Development (4 processes)
- Tool Use and Function Calling (3 processes)
- RAG (Retrieval-Augmented Generation) (4 processes)
- Memory Systems (2 processes)
- Prompt Engineering and Optimization (2 processes)
- Safety and Alignment (3 processes)
- Evaluation and Testing (4 processes)
- Deployment and Operations (4 processes)
- Voice and Multi-Modal (2 processes)
- Fine-Tuning and Customization (2 processes)
- Persona and Personality (2 processes)
- Agent-Specific Patterns (3 processes)

## Next Steps

Phase 3 will involve creating JavaScript process files for each identified process using the Babysitter SDK patterns, including:
- Process definitions with inputs/outputs
- Task definitions (node, agent, skill tasks)
- Quality gates and validation steps
- Iterative refinement loops where applicable
- Integration with existing frameworks and tools
- Agent architecture patterns and implementations
- RAG pipeline components and optimization
- Safety and evaluation frameworks
