Agents on the Order Book: that’s a wrap. 🤖 At Web Summit Lisbon, Rockman walked through BYBIT-EU/skills and how AI-assisted trading workflows can stay transparent, inspectable and under user control. Thanks to everyone who joined.

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:

Agents need a computer of their own, but VMs boot slowly and containers isolate weakly. How did Google resolve that trade-off? The options were two. VMs give root access and strong isolation, but starting or resuming one takes seconds to minutes. Containers are fast and light, but they share the host kernel, so they can't give a coding agent enough isolation. Tearing everything down each turn loses working state, and keeping a dedicated VM running for every waiting agent wastes resources. 📌 The turning point was two observations: agents mostly wait, and the sandbox should hold no secrets. Google measured that agents spend most of their time waiting on the model or the user. So Agent Compute Engine (ACE) checkpoints a session with its state intact once a tool call finishes, using near-zero active resources while idle. Meanwhile, the base OS and browser are shared in physical memory through DAX and CoW. The security stance is to assume everything inside the sandbox is controlled by an attacker. The guest has no secrets and no standard network card, and outbound traffic is checked by Agent Gateway, which swaps in credentials on the wire. Even with root compromised, there is nothing to steal. Preview is targeted for "the coming months," and the post gives no startup times or pricing. Details are in Inside Agent Compute Engine: Building the compute foundation for the agentic era. #AIAgents# #GoogleCloud#

Agents shouldn't have to relearn the same facts every session. Agent Memory on Agent Bricks combines session history with distilled long-term memories. A background pipeline extracts durable facts, removes duplicates, and replaces information contradicted by newer conversations. Lakebase provides low-latency access. In our internal benchmarks of enterprise data tasks, this memory architecture tripled agent pass rates while using 31% fewer tokens compared to agents without memory.