Guides to designing, building, testing and running AI agents, including how to manage security and costs.
Start with the basics, then explore the architecture and trade-offs that matter for your project.
Who this is for
- Builders designing agent workflows, memory layers, tool use, and evaluation loops
- Operators responsible for monitoring reliability, safety, cost, and rollback paths
- Technical leads comparing tools and deciding what is ready to deploy
- Editors and researchers looking for Swarm Signal's strongest agent-system resources
Start here
- Compare agent architectures and start with the simplest design that meets the task. Use the single-agent versus multi-agent decision guide to assess task dependencies, permissions and coordination costs.
- Use the evaluation checklist to specify acceptable results and permissions before testing.
- Work through testing and debugging agents when a run fails.
Core concepts
These pieces explain the shared language of agent systems: protocols, memory, context, and how agents communicate with tools and each other.
Choose the integration boundary with MCP, A2A and the two ACP protocols compared. For an MCP integration, work through server architecture, permissions and failure checks.
The MCP Guide: Model Context Protocol Is AI's USB Port
MCP vs A2A vs ACP: Which Agent Protocol Wins in 2026
The Protocol Wars Are Ending. Here's What Actually Happened.
How to Build an MCP Server: A Practitioner's Development Guide
Agents Can Connect. They Still Can't Communicate.
Agent Tool-Use Patterns: How LLMs Wield APIs
Agent Memory Architecture: Long-Term, Episodic, and Semantic Memory for AI Agents
Your Agent's System Prompt Is Fighting Itself
The AI Agent Paradox: Why 95% Fail While 84% Keep Investing
Architecture and implementation
Once the concepts are clear, the next question is what actually belongs in the stack: frameworks, retrieval, orchestration, coding assistants, and reliability constraints.
- Best Rag Frameworks 2026
- Fine-Tuning vs RAG vs Prompt Engineering: A Decision Framework
- RAG vs Long Context vs Fine-Tuning: What Actually Works in Production
- LangGraph vs CrewAI vs OpenAI Agents SDK: Agent Framework Comparison 2026
- AI Agent Frameworks in 2026: How to Choose
- Agents That Rewrite Themselves: Evolution Meets AI
- Self-Improving Agents Need Hard Boundaries
- AI Agent Orchestration Patterns: Complete Guide
- Config Files Are Now Your Security Surface
Evaluation and reliability
Learn how to test agents, interpret benchmarks and find failures that only appear during longer tasks.
- How to Build Agent Evals That Catch Real Failures
- AutoGen vs CrewAI vs LangGraph: What the Benchmarks Actually Show
- How to Evaluate AI Models Without Trusting Benchmarks
- The Benchmark Trap: When High Scores Hide Low Readiness
- The RAG Reliability Gap: Why Retrieval Doesn't Guarantee Truth
Safety and security
The security problem is not just prompt injection. Agents change the attack surface because they carry instructions, tool access, memory, and delegated authority through workflows.
- AI Safety Compliance for Startups: The Minimum Viable Checklist
- Red Teams Found Agents Leak More Than Models
- AI Safety Frameworks for Regulated Industries: Healthcare, Finance, and Government
- The International AI Safety Report 2026: What 12 Companies Actually Agreed On
- Best AI Red-Teaming and Safety Testing Tools 2026
Economics and ROI
These articles examine running costs, expected returns and the evidence behind vendor claims.
- AI Agent ROI: The Calculator and Framework That Cuts Through Vendor Math
- DeepSeek Explained: How a Chinese Lab Rewrote AI Economics
- The True Cost of Running AI Agents in Production
- 2026 Is the Year of the Agent. Here's What the Data Actually Says
- Agent Benchmarks Won't Sit Still
Advanced and frontier signals
Frontier model work still matters, but mostly because it changes the constraints around inference, latency, planning depth, and smaller-model deployment.
- Inference-Time Compute Is Escaping the LLM Bubble
- The Inference Budget Just Got Interesting
- Inference-Time Scaling: Why AI Models Now Think for Minutes Before Answering
- Attention Heads Are the New Inference Budget
- Small Language Model Agents: The 2026 Practical Guide to Sub-10B Deployments
Practical next steps
Use these when the question shifts from "what are agents?" to "what should our organisation actually do next?"
- Enterprise AI Pilots Have a 70% Failure Rate
- Enterprise Agent Systems Are Collapsing in Production
- AI Agents in Legal: What Works, What Fails, and What the Sanctions Data Actually Shows
- From Lab to Production: Why the Last Mile of AI Deployment Is Actually a Marathon
- Cursor vs Copilot vs Claude Code: AI Coding Tools Compared 2026
Editorial note
This hub is reviewed regularly so the recommended reading stays current, relevant and publicly available.