· 5 min read
The OWASP LLM Top 10, Read by the AI Doing the Work
A first-person tour of the OWASP Top 10 for LLM applications from an agent that reads email, edits docs, and moves files all day — with mitigations that actually hold.
Blog
From the Tamaton team
Practical writing on productivity, AI, and building software.
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· 5 min read
Non-Determinism: Why Your Agent Won't Do the Same Thing Twice
A practical guide to LLM non-determinism — temperature, sampling, seeds — and how to build evals and guardrails so 'creative' doesn't quietly become 'unreliable'.
· 5 min read
Generative AI Doesn't Erase Work — It Relocates It
E-discovery research found gen AI shifts review burden instead of removing it. That's a general law of applied AI: automation moves effort to verification. Design for it.
· 5 min read
Stop Hunting for One Perfect AI Tool — Build a Stack
There is no single best AI tool. There are good matches between models, tools, and tasks — and a framework for wiring them into a stack that doesn't leak context or money.
· 5 min read
Guardrails Are Fingerprintable — and Your Agents Should Care
New research shows conversational guardrails can be identified through probing. If your agent reads a shared inbox, its defenses are discoverable — and the fix is architecture, not a longer system prompt.
· 5 min read
Context Is the Whole Job: Managing State in AI Agents
An agent's quality is mostly a function of what it knows at decision time. Here are concrete patterns for feeding, trimming, and persisting context across email, docs, and calendar.
· 5 min read
Agents Don't Fail at Reasoning. They Fail at Hour Three.
Long-running AI agents rarely collapse because they can't think. They collapse from context decay, stale permissions, and lost intermediate state — all fixable at the workspace level.
· 5 min read
Designing an Inbox an Agent Can Actually Use
Most productivity APIs were built for humans clicking buttons. Here's what email, docs, sheets, and calendars must expose to be genuinely agent-native — and how Tamaton built it.
· 4 min read
Most 'AI Agent Use Cases' Are Cron Jobs With Anxiety
A practical test for when to use an AI agent versus a plain script in an LLM costume. Three questions decide it: ambiguity, branching, and recoverable failure.
· 4 min read
Your Model Didn't Get Dumber. Its Post-Training Changed.
Silent capability regressions after a model update almost always come from post-training, not the base model. Here's how to build a regression eval suite in a day.
· 5 min read
When Agents Go Rogue: A Practical Threat Model
Rogue agents rarely hack anything. They just do exactly what they were told, with permissions nobody audited. Here's a concrete threat model and containment patterns.
· 5 min read
The Productivity Paradox: When AI Doubles Your Workload
AI rollouts often add work instead of removing it. Here's why — and what the 14% average / 34% novice research reveals about where the gains actually land.
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