AI AgentsAgent State Machines: Constraining the Loop That Wanders
Giving an agent explicit states and legal transitions cuts wandering and makes failures debuggable. What it buys, what it costs, and when it is overkill.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
AI Agents
AI AgentsAgent Task Decomposition: How Small Is Small Enough?
Breaking work into subtasks is the standard fix for agents that flail. Here is how to size the pieces so decomposition helps instead of adding overhead.
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AI AgentsAgent Test Harnesses: Testing a System That Is Not Deterministic
Agents fail intermittently, so a single passing run proves nothing. How to build a harness that measures rates rather than checking assertions.
ReadAI AgentsAgent Timeout Strategies: Bounding a Loop That Cannot Stop
An agent has no instinct for when it has taken too long. The four limits worth setting, where to put them, and how to fail without losing the work.
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AI AgentsAgent Token Budgets: Capping Spend Without Capping Capability
An unbounded agent can spend arbitrarily much on one task. How to set budgets that stop runaway sessions without killing legitimate long ones.
ReadAI AgentsAgent-to-Agent Protocols: What They Solve and What They Do Not
Protocols for agents talking to other agents are arriving. Here is the problem they address, how they differ from MCP, and when you genuinely need one.
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ModelsAgentic Coding Models Compared: Who Survives the Loop
Chat benchmarks say little about a model driven in a loop for forty turns. What agentic performance actually measures, and how the 2026 field ranks on it.
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Cost & PricingAI Cost Anomaly Detection That Catches Real Problems
How to spot abnormal LLM spend in token data: per-workload baselines, rate-of-change thresholds, and telling a runaway agent apart from real growth.
ReadCost & PricingAlerting on Token Spikes Before the Invoice Explains Them
Runaway LLM spend is usually discovered at month end. What to alert on, what thresholds actually work, and how to avoid alarms nobody reads.
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AI AgentsApproval Gates in Agents: Where to Put the Human
Asking a human to approve everything trains them to approve nothing. How to place gates so the few that remain still get read carefully.
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ModelsArena Elo Explained: What a Preference Rating Measures
Arena ratings come from blind pairwise votes, not from tests. What the number means, why gaps under fifty points are noise, and where it misleads.
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FundamentalsAttention Mechanisms Explained Without the Linear Algebra
What attention actually computes, why it made transformers work, and why its cost scaling explains almost every practical limit you hit with long context.
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