The AgentOS: A Platform Approach to Running Agent Fleets That Scale with Your Business by @sobtiankit, Co-Founder @getpostman Join us at the BoxWorks Dev Summit for Ankit’s talk on moving from impressive agent demos to fleets that can be deployed safely alongside your data and people.

Today, WorldClaw launches WorldRouter with @worldlibertyfi, one simple account to access 300+ AI models with competitive fees*. No more jumping between ChatGPT, Claude and all the others. Same power, way cheaper. This is your first step into the WorldClaw AgentOS. 👉 #USD1# #WLFI# #AI# #AgentOS# #WorldClaw# #WorldRouter# *WorldRouter rates shown are priced approximately 30% below the corresponding model providers' published list rates at the time of publication. See website for more pricing details.

Agents that know your business. Trusted to act. Better with every run. Here’s how Snowflake CoCo and Snowflake CoWork help you build faster and work smarter👇 #SnowflakeExpedition#

Agents can build software. But what about the databases behind it? Databricks VP of Engineering and @neondatabase co-founder @nikitabase joined @MTSlive to talk about database branching, serverless architecture, and building for both AI agents and enterprise workloads.

agents hit @firecrawl over a billion times a month those requests show us exactly what data agents need, and that demand decides what goes on alexandria’s shelves give your agents the library they’re missing 🔥

AgentScope introduces Piccolo: open-weight decision models in 27B, 9B, and 4B sizes for agent and data workflows. Piccolo evaluates candidate options in context and returns structured decisions with probabilities. 🤖 🌐 Explore Piccolo: 🎯 Decisions that drive action: Control game characters, select and review tools, retain relevant context, filter data, assess output quality, and choose the next step—all through one decision interface. 📊 Benchmark results: Piccolo-27B achieves 92.21% accuracy on JevBench Public and scores 68.59 and 77.75 on text and multimodal Decision Index Public, respectively. On our purpose-built benchmarks, it achieves 84.41% on HarnessJevBench for agent execution control and 82.60% on DataJevBench for data workflows. 🔄 Recursive self-improvement: Model errors and per-example learning signals guide data selection, synthesis, and changes to training and inference. Research agents test each change and use the results to plan the next round, within human-defined goals and budgets. 📜 Apache 2.0; third-party licenses apply. 💻 Code: