Agentic AI Hub

Agentic AI marks the shift from passive Large Language Models (LLMs) to autonomous systems capable of planning, using tools, and achieving goals through iterative execution. This hub organizes Wikantik's content on the engineering of reliable, observable, and capable agentic systems.

Architecture and Loops

The structural frameworks that define how an agent operates.

• AI Agent Architectures — Survey of ReAct, Plan-and-Execute, and other core architectural patterns
• Agentic Architecture — High-level design for integrating LLMs into larger software systems
• Agentic Workflow Design — Moving from single-shot prompts to multi-step, iterative processes
• Flow Engineering — Moving beyond ReAct loops to deterministic, DAG-based state machines (AlphaCodium pattern)
• Agent Loops — Designing the cognitive loops that drive agent behavior
• Multi-Agent Orchestration — Coordinating groups of specialized agents to solve complex problems

Cognitive Capabilities

The internal processes that enable agents to reason and act.

• Agent Reasoning — Techniques for zero-shot, few-shot, and Chain-of-Thought reasoning
• Chain of Thought Reasoning — Explicitly modeling the step-by-step reasoning process
• Test-Time Compute Scaling — System 2 inference scaling via Language Agent Tree Search (LATS) and PRMs
• Agent Planning — Decomposing complex goals into actionable sub-tasks
• Agent Memory — Short-term context management and long-term knowledge retrieval
• Context Window Management — Strategies for working within the finite limits of LLM attention
• Efficient Context Passing — Managing the attention bottleneck and context stacks
• Context Compression — Technical methods for maximizing information density

Tool Use and Skills

Expanding an agent's capability through external integration.

• AI Function Calling and Tool Use — The technical bridge between natural language and structured APIs
• Custom Skills Architecture — Designing modular, reusable capability blocks for agents
• Advanced Skill Patterns — Complex interaction patterns for high-capability tools
• Agent Prompt Engineering — Crafting the system instructions that define agent persona and constraints

Reliability and Engineering

Tools and practices for making agents production-ready.

• Agent Testing — Strategies for evaluating non-deterministic agent behavior
• Agent Observability — Monitoring the internal state and external actions of running agents
• AI Evaluation and Benchmarks — Measuring capability and regression across model and prompt changes
• AI Hallucination Mitigation — Engineering safeguards to keep agents grounded in fact
• Federated Knowledge Graphs — Multi-source knowledge integration for complex agent reasoning
• AI Data Privacy and Compliance — Handling sensitive data in agentic workflows

Agent-Grade Content

• Agent-Grade Content Design — Structuring wiki content so it is consumable by autonomous agents
• Agent Cookbook (Runbooks) — High-quality procedural documentation for agent execution

Adjacent Hubs

• Generative AI Hub — The underlying model technologies
• ML Hub — Foundational machine learning theory
• Software Engineering Practices Hub — Engineering discipline for complex systems