Insights on AI agents, web automation, and building the perception layer for the next generation of AI applications.
Technical architecture for browser automation agents in regulated environments. Covers defense-in-depth patterns, SR 11-7 and NIST AI RMF compliance mapping, and multi-agent mandate delegation.
When you revoke an agent's authority, every agent downstream should lose theirs too. This is obvious. Most systems don't implement it. Learn why cascade revocation is essential for secure multi-agent delegation.
Policy enforcement answers one question: can this action execute? But there's a second question that matters more for correctness: did reality actually change? This post explains why the distinction matters.
The problem isn't that your agent is insufficiently instructed. The problem is that instruction compliance is voluntary. Learn why infrastructure-enforced boundaries beat prompt engineering for agent safety.
Knowing who the agent is doesn't tell you what the agent should be allowed to do right now. Learn why OAuth isn't enough for AI agents and how mandates solve the delegation problem.
Agents are probabilistic actors operating inside deterministic systems. Every action is a small game of chance. As agents touch more production infrastructure, the failure model is changing—and so must the controls.
Current AI agent frameworks are inherently insecure by design. Security must move from the planning layer to the execution layer. Here's how Runtime Trust Infrastructure solves the problem.
Deep dive into Predicate Secure - a drop-in security wrapper with pre-execution authorization and post-execution verification using local LLMs. Integrates with browser-use, LangChain, PydanticAI, and more in 3-5 lines.
How ML-powered DOM pruning reduces OpenClaw agent token costs from 600K to 1.3K per page observation—without losing actionable elements.
Prompt engineering has hit a ceiling. The next reliability leap comes from runtime verification: assertions, traces, and loud failures.
Agents don't fail because they reason badly. They fail because they act without accountability. This post explains the architectural pattern that fixes it.
A technical case study on apa.org showing how verification-first execution + JSONL traces make browser agents debuggable (candidate coverage, no-ops, auth drift, extraction grounding).
How AgentRuntime traces + snapshot diagnostics turn 'it closed by itself' into actionable fixes (overlays, verification, typing, read injection).
How structured snapshots + Jest-style assertions make small local models reliable.
How structure-first snapshots and assertions cut browser agent token usage by ~50% and make small local models viable.
Run web agents on 3B–14B local models by replacing screenshots + raw DOM with structure-first snapshots — cutting token usage by ~50% per step.
Modern LLM agents struggle on the web because the web is not a static document—it's a live, unstable system. This post explains why structure + stability + verification beats raw DOM, accessibility trees, or vision-first approaches.
The Accessibility Tree (AX) provides standardized roles, names, and states—but reliable agents need more. This post explains where AX shines, where it falls short, and why geometry + stability + verification are required.
Why logs and screenshots are not enough for AI agents — and how trace-based observability enables replay, determinism, and real debugging.
Vision models are good at seeing, but agents fail at acting. This post explains why vision-first web agents break down in practice, and how semantic geometry enables reliable execution.
How we solved the '4,500 elements problem' and saved customers thousands in token costs without sacrificing accuracy.
Building AI agents that can truly see and understand the web requires more than just scraping HTML. Learn how Predicate provides visual grounding for large action models.
Traditional headless browsers are slow and expensive. We built an adaptive hybrid architecture that delivers 10x faster performance at 90% lower cost.
Not all web automation tasks are created equal. Learn when to use our Performance Engine for speed and when to use Precision Engine for accuracy.
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