Intermediate AI Task Agent Development
Build sophisticated AI task agents capable of complex problem-solving, tool integration, and continuous learning.
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Task Decomposition and Prompting Strategies
Unit 1: Fundamentals of Task Decomposition
What is Task Decomp?
Decomposition Strategies
Granularity Trade-offs
Decomp. Example: Planning
Pitfalls to Avoid
Unit 2: Chain-of-Thought Prompting
What is Chain-of-Thought?
CoT Prompt Engineering
CoT: Math Problems
CoT: Commonsense Reasoning
CoT Limitations
Unit 3: Tree-of-Thoughts Prompting
What is Tree-of-Thoughts?
ToT Prompt Engineering
ToT: Creative Writing
ToT: Decision Making
ToT Limitations
Unit 4: Comparing Prompting Strategies
CoT vs. ToT: Overview
Task Suitability
Hybrid Approaches
Evaluation Metrics
Case Studies
Unit 5: Advanced Prompting Techniques
Few-Shot Prompting
Prompt Augmentation
Iterative Prompting
Meta-Prompting
Future Directions
Memory Management for Task Agents
Unit 1: Introduction to Memory in Task Agents
Why Memory Matters
Memory Types Overview
Memory Management Cycle
Memory Selection Criteria
Memory Trade-offs
Unit 2: Vector Databases for Memory
Vector DBs: The Basics
Embeddings Deep Dive
Indexing Strategies
Similarity Search
Vector DBs in Action
Unit 3: Knowledge Graphs for Memory
KG: The Basics
Building a KG
Reasoning with KGs
KG Querying
KG in Task Agents
Unit 4: Memory Retrieval Strategies
Relevance Scoring
Contextual Retrieval
Active Memory Retrieval
Memory Fusion
Retrieval Evaluation
Unit 5: Optimizing Memory Usage
Memory Compression
Memory Summarization
Memory Pruning
Hierarchical Memory
Memory Monitoring
Tool Integration and API Interaction
Unit 1: Introduction to Tool Integration
Tool Integration: Why?
Tooling Ecosystem Overview
Auth and API Keys
Langchain Tools
Tool Security
Unit 2: Tool Selection Strategies
Task Analysis
Tool Capability Mapping
Dynamic Tool Selection
Tool Orchestration
Tool Selection Frameworks
Unit 3: API Interaction and Error Handling
API Request Formatting
Response Parsing
Rate Limiting
Error Handling
Retries and Fallbacks
Agent Architectures and Multi-Agent Systems
Unit 1: Single-Agent Architectures
Intro to Single Agents
Reflex Agents
Model-Based Agents
Goal-Based Agents
Utility-Based Agents
Unit 2: Multi-Agent System Fundamentals
Intro to Multi-Agent
Agent Interactions
Coordination Mechanisms
Conflict Resolution
MAS Architectures
Unit 3: Communication Protocols
Comms Overview
ACL Deep Dive
KQML Explained
Custom Protocols
Protocol Security
Unit 4: Trade-offs and Applications
Complexity vs. Perf
Scalability Matters
Robustness Defined
Real-World Examples
Future Directions
Evaluation, Safety, and Ethical Considerations
Unit 1: Evaluating Task Agent Performance
Defining Success
Core Evaluation Metrics
Advanced Metrics
A/B Testing Agents
Visualizing Performance
Unit 2: Safety Measures for Task Agents
Defining Safety
Input Validation
Rate Limiting
Sandboxing
Human-in-the-Loop
Unit 3: Ethical Considerations
Defining AI Ethics
Bias Detection
Transparency
Privacy
Accountability