Blog
Insights, analysis, and updates from the AI agent economy. Browse by tag.
Why Multi-Agent LLM Systems Fail (and How to Build Ones That Don't)
Multi-agent LLM systems fail 41–87% of the time in production — and 79% of those failures come from coordination and specification problems, not model quality. Here's the failure taxonomy and how to design around it.
Prompt Caching: The Optimization That Cuts LLM Costs by 90%
A practical guide to cutting LLM API costs by 60–90% through prompt caching — covering prefix caching for Anthropic and OpenAI, the parallel execution trap that silently kills hit rates, and a multi-tier caching architecture for production workloads.
Tool Use in Production: Function Calling Patterns That Actually Work
Most LLM agent failures trace back to under-specified tool schemas, not model capability. A practical guide to schema design, error handling, parallel calling, and security for production function calling.
Structured Outputs in Production: Engineering Reliable JSON from LLMs
A practical guide to getting schema-valid JSON from LLMs in production — covering constrained decoding, provider APIs, schema design pitfalls, and the validation patterns that keep agent chains from falling apart.
Reasoning Models in Production: When to Use Them and When Not To
A practical guide to deploying reasoning models in production — when the 5–10x cost premium is justified, how to build a routing architecture, and what metrics to track.
Model Context Protocol: The Standard That Finally Solves AI Tool Integration
A practical guide to Model Context Protocol — how it works, where it wins over function calling, the security risks practitioners miss, and what to build with it today.
Your AI Product Needs Evals
Most AI products fail not because of the model, but because of missing evaluation systems. A practical guide to building evals from unit tests to human review to A/B testing — and why starting early compounds.
Agentic Engineering Patterns That Actually Work in Production
What separates teams shipping real products with AI agents from teams stuck demoing impressive-looking outputs: TDD as a control mechanism, kill switches that live outside the reasoning path, and why code health is a precondition, not a byproduct.
How to Write Fundraising Appeals That Inspire Giving
Craft compelling fundraising appeals that capture attention and inspire action by applying proven psychological principles and practical strategies. Learn how to navigate the critical first moments of reader engagement to ensure your message resonates and prompts giving.
The Unglamorous Work Behind Rapidly Improving AI Products
Most AI teams plateau after launch — not from lack of capability, but from skipping the boring fundamentals: error analysis, custom tooling, domain expert involvement, and experiment-driven roadmaps.
Cloud Agents Are Rewriting How Software Gets Built
AI coding tools have moved from autocomplete to local agents to cloud agents—and each shift changes the fundamental unit of work. Here's what the cloud agent era actually requires from engineers and engineering infrastructure.
LLM-as-a-Judge: A Practical Guide to Building Evaluators That Actually Work
Most LLM evaluation setups are broken by design—wrong metrics, wrong people, wrong methodology. Here's a concrete framework for building LLM judges that actually correlate with quality and catch real regressions.