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VentureBeat

Anthropic says it hit a $30 billion revenue run rate after 'crazy' 80x growth OpenAI voice models get GPT-5-class reasoning AI agent identity: how to govern agentic AI in 6 stages Anthropic wants to own your agent's memory, evals, and orchestration — and that should make enterprises nervous Enterprise GPU utilization: why 95% of AI infrastructure spend is wasted Governance, not gatekeeping: How SAP brings enterprise‑grade safety to AI connectivity Anthropic introduces "dreaming," a system that lets AI agents learn from their own mistakes RL orchestration: how a 7B model routes tasks across GPT-5, Claude, and Gemini Meet ZAYA1-8B, a super efficient open reasoning model trained on AMD Instinct MI300 GPUs Anthropic Skill scanners passed every check. The malicious code rode in on a test file. Why AI breaks without context — and how to fix it Market research is too slow for the AI era, so Brox built 60,000 identical 'digital twins' of real people you can survey instantly, repeatedly The app store for robots has arrived: Hugging Face launches open-source Reachy Mini App Store with 200+ apps Scaling AI into production is forcing a rethink of enterprise infrastructure Miami startup Subquadratic claims 1,000x AI efficiency gain with SubQ model; researchers demand independent proof. GPT-5.5 Instant shows you what it remembered — just not all of it One command turns any open-source repo into an AI agent backdoor. OpenClaw proved no supply-chain scanner has a detection category for it AI agents are missing all the discussions your team is having. SageOX has an answer: agentic context infrastructure OpenAI turns its sold-out GPT-5.5 party into a monthlong Codex giveaway for 8,000 developers Inside AMEX’s agentic commerce stack: How intent contracts and single-use tokens enforce AI transactions Microsoft takes Agent 365 out of preview as shadow AI becomes an enterprise threat The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next Salesforce Agentforce Operations fixes workflows breaking enterprise AI MCP command execution flaw: what security teams need to know The scaffolding era is over. LlamaIndex says context is the new moat xAI launches Grok 4.3 at an aggressively low price and a new, fast, powerful voice cloning suite Hidden IT problems are quietly creating risk, shadow IT, and lost productivity Alibaba's HDPO cuts AI agent tool overuse from 98% to 2% One tool call to rule them all? New open source Python tool Runpod Flash eliminates containers for faster AI dev Why OpenAI's 'goblin' problem matters — and how you can release the goblins on your own
Amazon will present its framework for engineering trustwo...
Bridget Botelho · 2026-06-25 · via VentureBeat

AI agents are increasingly proficient at executing business tasks autonomously, but IT leaders are cautious about granting permissions to access enterprise systems. 

Part of the challenge lies in how AI reliability is measured. Industry standards often rely on EVAL scores, which provide a static snapshot of performance rather than a measure of overall reliability. These metrics can fail to capture predictability across prompts, environments, and input types, said Bryan Silverthorn, director of the AGI Autonomy research lab at Amazon.

Amazon’s AGI autonomy research lab is moving beyond raw performance benchmarks, focusing instead on a structured framework centered on consistency, robustness, predictability, and safety, Silverthorn told VentureBeat during an interview ahead of his session at VB Transform 2026.

Rather than assuming that models can be harnessed into safety, Amazon’s approach emphasizes decoupled systems, such as sandboxed environments where agents propose changes that are reviewed by humans before implementation. 

This strategy aims to bridge the trust gap by prioritizing verifiable interactions, even in highly sensitive domains like finance, where the potential damage an agent can cause is significant.

In VentureBeat’s Q2 Pulse Research survey of over 100 senior technology leaders and buyers, just 4% said they are comfortable relying on model guardrails alone. When asked what worries them most about model guardrails, 40% said unauthorized access to tools or data and 27% cited prompt manipulation or injection.

At VB Transform, Silverthorn will share details of Amazon’s approach to trustworthy agentic AI and how companies can move from single-agent wrappers to multi-tool architectures that can self-correct mid-execution during his session titled Closing the capability-reliability gap: Inside Amazon’s framework for engineering trustworthy agents.

Another agentic ops and evals-focused session at VentureBeat’s flagship conference, happening July 14 and 15 in Menlo Park, is Intelligence at scale: How Waymo builds safe, efficient AI for the physical world with speaker Manasi Joshi, director of systems intelligence and machine learning at Waymo. 

Interested in attending VB Transform 2026? A select number of complimentary passes are also available to senior technology leaders. Contact us to get yours. You can also purchase tickets here.