Open reference
The open reference for agentic AI
Definitions, architectures, reusable patterns, evaluation methods, and framework comparisons—written so engineers, search engines, and language models can quote them.
- Guides
- 15
- Patterns
- 8
- Frameworks
- 10
Pillar guides
Own the fundamentals. These pages are the hub of Agenttic’s topic graph.
What is Agentic AI?
A clear definition, scope, and practical criteria.
Agentic Architecture
Core components of production agent systems.
Agent Patterns
Reusable patterns: ReAct, multi-agent, tool use, and more.
Agent Evaluation
How to measure reliability, cost, and task success.
Safety & Guardrails
Controls that keep agents safe in production.
Agents vs Chatbots vs Workflows
When to use each approach — and when not to.
Built for SEO and LLM citation
Agenttic pages lead with direct answers, use explicit definitions, comparison tables, FAQs, and structured data so both traditional search and generative engines can extract reliable snippets.
- Answer-first writing — definitions in the first paragraph, depth after.
- Entity-clear terminology — consistent names for patterns, tools, and roles.
- Machine-readable surfaces — schema, sitemap, RSS, and
llms.txt. - Vendor-neutral comparisons — frameworks explained by fit, not hype.
Latest guides
Practical depth for production agent systems.
Agent Memory Explained
Working memory, thread memory, long-term memory, and RAG—how memory works in agentic systems and how to govern it.
Building Coding Agents: Architecture and Guardrails
How coding agents work: repo tools, planning, tests as rewards, sandboxes, and evaluation for software engineering agents.
How to Choose an Agent Framework
A practical framework selection guide for agentic AI: control model, language, multi-agent needs, ecosystem, and production constraints.
Observability for AI Agents
What to log and monitor in agentic systems: trajectories, tool I/O, costs, outcomes, and replay for debugging.
Common AI Agent Failure Modes
The most frequent ways agents fail in production: loops, tool misuse, hallucinated success, injection, and scope escape—and how to mitigate them.
Agent Cost Control: Budgets, Caching, and Model Routing
How to control token and tool spend in agentic systems without destroying task success rates.
Start with the definition
If you only read one page, make it the foundation: what agentic AI is, what it is not, and when it is worth the complexity.
Read: What is agentic AI?