Agentic Systems Intelligence
The building blocks of AI, explained properly.
Agents. Reasoning. Memory. Safety. Architecture. Research-backed analysis for practitioners who build, not just browse.
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Categories
Six research verticals covering the full AI agent stack.
Agent Design
Architectures, tool use, and frameworks for building agents.
- The Agent Project That Should Have Been One LLM Call
- How to Build Agent Evals That Catch Real Failures
- Small Language Model Agents: The 2026 Practical Guide to Sub-10B Deployments
Swarm Systems
Multi-agent coordination, swarm intelligence, and collective behavior.
- Multi-Agent Systems Are Booming — But 76% of Deployments Fail Within 90 Days
- Multi-Agent Systems for Supply Chain Optimization
- Multi-Agent Communication Protocols: How Agents Actually Talk to Each Other
Reasoning & Memory
Reasoning tokens, RAG, context engineering, and memory systems.
- Knowledge Graphs for AI Agents: Beyond Vector Search
- Obsidian's CLI Turns Your Second Brain Into an API
- The RAG Reliability Gap: Why Retrieval Doesn't Guarantee Truth
Safety & Governance
Red teaming, bias, interpretability, and benchmarks.
- Open Source AI Impact: Who Wins When Models Get Cheap
- Interpretability as Infrastructure: Why Understanding AI Matters More Than Controlling It
- The Red Team That Never Sleeps: When Small Models Attack Large Ones
Models & Frontiers
Model comparisons, training data, open source, and research frontiers.
- How to Build an MCP Server: A Practitioner's Development Guide
- Inference Optimization: From 10x Cost to 10x Speed
- Model Selection Guide: How to Pick the Right AI Model for Your Use Case
Real-World AI
Enterprise deployment, workforce impact, and developer tools.
- Test-Time Compute in 2026: The Complete Practitioner's Guide
- Agent Cost Optimization: How to Track and Reduce LLM Spend
- From Lab to Production: Why the Last Mile of AI Deployment Is Actually a Marathon
Latest
Most recent articles across all categories.
The Agent Project That Should Have Been One LLM Call
title: "The Agent Project That Should Have Been One LLM Call"
Open Source AI Impact: Who Wins When Models Get Cheap
Open source AI used to be the cheaper substitute. In 2026, that is too small.
Test-Time Compute in 2026: The Complete Practitioner's Guide
Something shifted quietly in 2025. The dominant question in AI stopped being "how do we train a bigger model?" and became "how do we get more out of inference?"
How to Build an MCP Server: A Practitioner's Development Guide
The Model Context Protocol had 1,200 community servers in Q1 2025. By April 2026 that number hit 9,400. Ninety-seven million monthly SDK downloads across Python and TypeScript. First-class support in Claude, ChatGPT, Cursor, VS Code, and Microsoft Copilot. 78% of enterprise AI teams report at lea...
How to Build Agent Evals That Catch Real Failures
Your agent passes every benchmark. It scores 94% on tool-call accuracy. Your unit tests are green. Then it deletes the wrong records in production.
Small Language Model Agents: The 2026 Practical Guide to Sub-10B Deployments
In February 2025, using a small model as an autonomous agent felt like a compromise: you got cheaper inference but accepted meaningful capability loss on planning, tool selection, and multi-step reasoning. That trade-off calculus has flipped.