Insights on AI agents, web automation, and building the perception layer for the next generation of AI applications.
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 Sentience 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.
Get the latest articles on AI agents, web automation, and developer tools delivered to your inbox.