Guides and analysis for people building, running and choosing AI systems.
Browse by topic to find explanations, comparisons and practical guides.
Run an Engineering Lab
Run practical experiments in answer selection, retrieval, safe retries, outbox recovery and filtered vector search. Each includes code, fixtures and observed results.
Explore the Engineering Lab series
What are you working on?
- Choosing an agent design: compare agent architectures and use the routing example to test your choice.
- Testing a release: use the evaluation checklist and copyable test record to define success, permissions and failure cases.
- Keeping useful information between sessions: start with agent memory architecture.
- Running out of context: use the context window management guide to review what enters each request.
- Choosing a model: learn how to compare models on your own tasks.
Resource hubs
AI Agent Systems
Practical resources for designing, evaluating, securing and deploying agentic systems.
AI Safety, Evals & Guardrails
Evaluation, reliability, guardrails, security and governance for production AI systems.
Models & Frontiers
Frontier model releases, open-weight competition, inference patterns, benchmarks and model strategy.
Enterprise AI Operations
Deployment, reliability, governance, cost control and operating practice for teams moving AI systems from pilots into production.
Agent Memory & Context Engineering
Memory, context engineering, retrieval and RAG resources for teams building durable AI systems.
How to use Swarm Signal
- Start with the hub closest to the system you are building or evaluating.
- Explore related topics when your project also involves safety, deployment, model choice or memory.
- Check each article’s sources and limitations before applying its findings to your project.
- Use the guides to prepare for a deployment or compare tools before buying.
What you can read
- Guides for detailed explanations and practical advice.
- Topic pages that bring related articles together.