Practices for embedding AI agents into enterprise systems [Human-in-the-Loop Approval Gate] 💡 The last line of defense for AI is a human. Without an approval gate before high-risk actions, a single hallucination can cause irreversible damage. 🔥 Problems Solved - Critical errors from hallucination or misoperation execute with no final check - No audit trail of "who approved what" makes accountability impossible - Cannot comply with regulations requiring human involvement in certain operations - Approval scope too broad leads to rubber-stamping and approval fatigue 🏗️ Proposed Pattern Actions are risk-scored across dimensions like monetary value, blast radius, reversibility, and data classification. Only those exceeding the threshold enter the approval queue. Notifications go via Slack, email, or dedicated UI, while jobs are suspended and persisted during approval wait. Approval, rejection, or modification results are logged with approver identity for audit. Start with broad approval coverage, then progressively raise automation as accuracy track record builds (HITL to HOTL to full-auto). ✅ Selection Criteria - Fit: high-risk operations involving money, contracts, customer touchpoints, HR, production changes - Not Fit: low-risk, high-volume, latency-critical processing where approval becomes a bottleneck ⚠️ Pitfalls - Making everything require approval causes "approval fatigue" and defeats the purpose - Forgetting to persist jobs during approval wait leads to timeout and job loss - Without predefined accuracy thresholds for raising automation, you stay manual forever 🛠️ Implementation Approach 1. Define risk-scoring logic (monetary value, blast radius, reversibility, data classification) as policies in OPA/Cedar to dynamically determine approval requirements 2. Implement approval notifications via Slack Bolt (or Teams Webhook) with interactive approve/reject buttons 3. Use Temporal's workflow suspension or Step Functions callback waiting to persist jobs during approval wait 4. Log all approval/rejection/modification results as audit records (approver, timestamp, reason) to CloudWatch Logs or Datadog 5. Predefine HITL-to-HOTL-to-full-auto migration thresholds (e.g., 99%+ accuracy over 100 consecutive decisions) and visualize progress on a dashboard #AIAgents# #EnterpriseArchitecture#

Exponential growth across 2 dimensions, users and tokens per user, makes this AI era different from earlier buildouts, and allows AI labs' compute math to work (@fi56622380) (Visit Techmeme dot com for the link and full context!)

“DA layers really differ across three dimensions: performance, programmability, and AI-native design — because on-chain AI can’t operate in a world measured in mere megabytes per second.” @sachitakamura catches up with @michaelh_0g, Founder of @0G_labs, to break down how 0G compares with Celestia, Avail, and EigenDA: why throughput needs to increase by orders of magnitude, how to move beyond the broadcast bottleneck, and why a decentralized storage network is essential for ultra-fast data ingestion and retrieval.

“We really have delivered across all dimensions and divisions.” Following second quarter earnings, Chief Financial Officer Sarah Youngwood shares what’s driving Nasdaq’s strong execution and continued momentum.

🏆 Ryan's Choice 1st — @onehopeA9 Narrative Conviction Radar Scores narratives across 6 dimensions — social buzz, price lag, smart money growth, onchain confirmation, prediction market divergence, risk crowding. Picked for the most diverse usage of data in a single workflow. 2nd — @YiwiJR Interactive Airdrop Tier List 203 airdrop projects ranked by funding, social score, and status into S/A/R tiers. Filter by confirmed, potential, snapshot, claimable. Different from everything else in this thread — and that's the point. Try them yourself 👇

Human history is not so different from the Navier–Stokes equations. Smooth across its dimensions, unimaginably complex in the flow. Yet, under concentrated force it can collapse toward a singularity, as vortices stretch and the field diverges.

Did you know that when @axisrobotics delivers data to a robotics company, it does not hand over a pile of recordings. It delivers a Package. Each package is measured across four dimensions. → Scenario sets the environment, such as a kitchen countertop or industrial sorting. → Atomic Skills covers primitives like grasp, place, push, and rotate, and continuous skill chains carry more training value than isolated motions. → In task Randomization multiplies diversity across layouts and assets. For example, 40 layouts times 4 asset variants gives 160 instances. → Total Trajectories sets the volume, with packages ranging from 10,000 to 1,000,000. Every one of those trajectories comes from a person operating a robot in a browser.

AI voice agents can complete common phone scams in about 130 seconds. They succeed 36% of the time, at under USD 0.75 per successful scam. TRM's new report measures how AI is changing the economics of fraud across four dimensions — speed, scale, cost, and expertise — and provides recommendations for closing the capacity gap. Read the full report here ➡️

50 Years is a deep-tech VC firm investing in AI safety. They’re organizing a fun, intimate mini-conference designed to align participants across multiple dimensions. I’ll be speaking there.

（Claude、GPT、GLM） GLM-5.2 Tops Artificial Analysis as the #1# Open-Source Model, Ranking Top 3 Globally GLM-5.2 launched and went open-source today, delivering a solid scorecard across multiple authoritative third-party benchmarks and arenas. 📊 Artificial Analysis Intelligence Index A comprehensive evaluation that integrates several authoritative leaderboards spanning coding, reasoning, long context, and more. GLM-5.2 scored 51, ranking among the top of all available models—on par with Claude Opus 4.8—and claiming the #1# spot among open-source models worldwide. 🎨 Code Arena A real-world head-to-head arena focused on front-end code generation, with Elo rankings produced by blind user voting. GLM-5.2 ranked #2# globally with a score of 1,595. 🏆 DesignArena A category arena centered on scenarios that combine design and code. GLM-5.2 took the top spot with a score of 1,360. ⚙️ FrontierSWE A software-engineering benchmark built around the "frontier of human capability," assessing engineering ability across three dimensions: implementation, performance, and research. GLM-5.2 ranked #3# overall. 💪 From front-end development and design-to-code to engineering-grade software tasks, GLM-5.2 consistently lands in the top tier across multiple real-world evaluation scenarios, steadily closing in on the world's strongest models. We'll keep pushing forward in pursuit of an ever-higher ceiling of intelligence.