Topic

AI Agents

An AI agent is a model in a loop with tools: it decides what to do, does it, reads the result and continues until the job is finished. Agents are only as good as the context they receive and the checks around them. These guides cover how to build them, what to feed them and when a simpler model is the better choice.

4 guides on AI Agents.

Reference Guide · 38 min read

User-First Intelligence (UFI)

User-First Intelligence (UFI) starts with people, not keywords. It follows the people you serve to every place they ask for help (search engines, AI assistants, video, social platforms, communities and reviews), sorts what they say onto one map of your industry, and turns the gaps into decisions about what to build, publish or fix, in the format and on the platform each audience prefers.

User-First Intelligence Personas Search Demand Knowledge Graphs Ontology Classification Embeddings RAG AEO GEO AI Agents Content Strategy

Reference Guide · 60 min read

The Complete Guide to AI Agents for Marketing

Parts 1 to 4 explain what agents are and when to use one. Parts 5 to 8 build the first agent end to end. Parts 9 to 13 add five more, from a review reply drafter to a multi-client SEO audit swarm. Parts 14 to 18 cover cost limits, testing, success criteria, production, security and the law, and Part 19 is a checked reading list of 62 resources from Anthropic, OpenAI, Google, IBM, MIT and others. Every agent runs from the public companion repo, offline, for $0: github.com/VenkataPagadalaGIT/marketing-ai-agents.

AI Agents Agentic AI AI Agents for Marketing Marketing Automation Context Engineering Prompt Injection AI Evaluation MCP OpenRouter Python

Reference Guide · 17 min read

What is Jev

Jev, released by TypeSafe AI on 2026-09-15, decides instead of writing. 14 stations across 4 kinds of responsibility, a 12-step guided journey from a ticket to a routed queue and on into an agent harness, the three answer shapes (Choice, Score, Noul) as working instruments, pricing and context limits from the vendor's own pages, and two independent preprints on where it fails. Sources checked 2026-09-24.

Jev TypeSafe AI System One model AI Agents Classification Structured Outputs 3D Interactive AI Explainer

Reference Guide · 16 min read

Graph Types for AI Agents

The six structures behind modern AI search, taxonomy, ontology, knowledge graph, information graph, context graph, vector / embedding index, what each one actually is, when to use which, and how they combine into GraphRAG. One dataset, six structures, visualized in 2D and 3D.

Knowledge Graphs Vector Databases Ontology GraphRAG RAG Retrieval-Augmented Generation Context Graph Semantic Search AEO GEO AI Agents Schema.org

Keep learning AI Agents

5 Types of AI Agents: Autonomous Functions & Real-World Applications video · IBM Technology
A2A Protocol (Agent2Agent) Explained: How AI Agents Collaborate video · IBM Technology
AI Agents: Transforming Anomaly Detection & Resolution video · IBM Technology
Building AI Agents · LangChain (Full Tutorial) video · The Full Stack
Building more effective AI agents video · Anthropic
Change Agents with Ethan Mollick: How AI agents will reinvent productivity video · IBM Technology
Context Engineering vs. Prompt Engineering: Smarter AI with RAG & Agents video · IBM Technology
Explainable AI: Demystifying AI Agents Decision-Making video · IBM Technology

From the AI Roadmap. Every link was checked.

AEO Classification GEO Knowledge Graphs Ontology RAG 3D Interactive Agentic AI

All Guides