Speculative Agents Need a Commit Boundary
Speculative execution cuts agent latency only when prediction and commitment are separate operations.
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AI Agents Guides
Building useful AI agents is mostly systems work: choosing the right tools, supplying durable context, controlling the execution loop, and verifying outcomes. These guides focus on the engineering patterns that turn a model into a dependable software system.
Loop / Harness Engineering
Agent loops turn prompts into one part of a production harness: tools, state, checks, approvals, and exit conditions.
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Plain-Text Context Files for LLMs
Plain-text context files are the git-native, zero-ops way to steer AI agents.
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How to Build an MCP Server
A guide to building an MCP server for Markdown note taking
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Structuring tools for AI Agents
A short conceptual guide to organising tools in agentic workflows
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