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.
Categories
Six research verticals covering the full AI agent stack.
Agent Design
Architectures, tool use, and frameworks for building agents.
- Agent Reliability Scores Are Getting Worse, Not Better
- When to Build vs Buy Your Agent Orchestration Layer
- Agent Tool-Use Patterns: How LLMs Actually Wield APIs
Swarm Systems
Multi-agent coordination, swarm intelligence, and collective behavior.
- When to Use Multi-Agent vs Single-Agent Architecture: A Decision Framework
- Multi-Agent Communication Protocols: How Agents Actually Talk to Each Other
- Your Multi-Agent System's Biggest Problem Is Its Org Chart
Reasoning & Memory
Reasoning tokens, RAG, context engineering, and memory systems.
- When to Use RAG vs Fine-Tuning in 2026: A Practitioner's Decision Guide
- AI Evaluation Frameworks 2026: Why Benchmarks Keep Lying
- Best RAG Frameworks and Tools 2026: From Prototype to Production
Safety & Governance
Red teaming, bias, interpretability, and benchmarks.
- AI Safety Frameworks for Regulated Industries: Healthcare, Finance, and Government
- Best AI Red-Teaming and Safety Testing Tools 2026
- Alignment Works in English. In Japanese, It Backfires.
Models & Frontiers
Model comparisons, training data, open source, and research frontiers.
- Best Open-Weight Models for Production AI Agents 2026
- MoE vs Dense Models: A Practitioner's Decision Guide for 2026
- Inference Optimization in 2026: Where the Compute Actually Goes
Real-World AI
Enterprise deployment, workforce impact, and developer tools.
- AI Agents in Insurance: Claims, Underwriting, and Fraud Detection
- The Enterprise AI Adoption Playbook: What Actually Gets Agents to Production
- AI Agents in Financial Services: Compliance, Trading, and Operational Automation
Latest
Most recent articles across all categories.
AI Agents in Insurance: Claims, Underwriting, and Fraud Detection
Allianz's seven-agent system cut claim processing time by 80%. Lemonade automates 55% of claims. Meanwhile, 23 states enforce AI governance rules. Where AI agents are working in insurance, and where they're not.
Agent Reliability Scores Are Getting Worse, Not Better
SWE-Bench scores tick up every quarter, but production failure rates aren't dropping. A METR study found half of test-passing PRs wouldn't be merged. The more capable we make agents, the less reliably they behave.
Best Open-Weight Models for Production AI Agents 2026
Your agent framework doesn't matter if the model underneath it can't call tools reliably. We tested and ranked eight open-weight models specifically for agent use cases: tool calling accuracy, multi-step reasoning, context retention, hosting economics, and licensing terms.
Single Agent vs Multi-Agent: When Swarms Actually Help
The Architectural Crossroads: Defining the Paradigms As we move through 2025 and into 2026, the design of AI agent systems has crystallised around two dominant paradigms: the single, monolithic agent and the orchestrated multi-agent swarm. The choice between them is foundational, influencing everything from system latency and cost to robustness
EU AI Act vs US vs UK: Global AI Regulation Compared
Introduction: A Divergent Regulatory Landscape For AI developers and architects building the next generation of agentic systems, the global regulatory environment is no longer a distant concern. By 2026, the frameworks established in the European Union, the United States, and the United Kingdom will have matured, creating distinct operational realities.
RAG vs Long Context vs Fine-Tuning: What Actually Works
Introduction: The Evolving Toolkit for AI Applications As we move through 2025 and into 2026, the strategies for adapting large language models (LLMs) to specific tasks and knowledge domains have matured significantly. The initial rush to adopt a single methodology has given way to a more nuanced understanding that the