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OpenClaw 2.0 is here, ushering in the era of ‘multiplayer’ AI coding: What it means for enterprises

The viral fervor we saw earlier this year around OpenClaw, the open source AI harness that turns powerful language models into autonomous workers the user can message via their favorite channels (Telegram, iMessage, WhatsApp, Discord etc), has cooled off substantially from its peak in March 2026. But over the weekend, OpenClaw’s creator Peter Steinberger and …

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Software engineers’ new job isn’t writing code — it’s designing the boundaries AI agents can’t break

If you look at the commit histories of modern data platforms, something profound has shifted over the last two years. The friction of writing syntax has collapsed. With Cursor, Claude Code, and agentic workflows now living inside our Docker containers and IDEs, generating the first implementation of a distributed streaming pipeline or a complex API …

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Identity and permissions aren’t enough to govern AI agent behavior

Presented by Box Identity and permissions are no longer enough to secure enterprise AI agents. They govern what an agent can reach, not how it behaves once it starts working on its own, and an autonomous agent can turn legitimate access of enterprise data into unintended action in seconds. That gap is pushing enterprise AI …

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AI agents need their own identity before they need a gateway

Enterprise AI has entered a new era. Organizations are rapidly moving beyond assistants that answer questions to autonomous agents capable of reasoning, invoking tools, accessing enterprise applications, coordinating with other agents, and completing multi-step business workflows with minimal human intervention. This shift represents a fundamental change in how software operates. Traditional applications execute predefined logic …

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AI agents that pass authentication can still drift, expose data, or get memory-poisoned

There is a clear repeating trend in agent deployments: The gateway is the first control teams reach for, but it is the one they are least ready to run. This is because gateways sit on top of identity and attribution layers that are mostly not there. The first layer of risk is not hypothetical. In …

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Meta researchers taught an 8B AI model to match Claude Opus 4.5 — without the frontier price tag

Consider an AI agent tasked with a complex enterprise workflow like migrating massive batches of customer records from a legacy CRM to a cloud database. The agent cannot rely solely on its internal context window for a job spanning hours and depends on the runtime layer, aka the harness. This harness provides execution feedback, like …

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The three layers of agentic AI security: A defense-in-depth architecture for autonomous agents

Presented by Nutanix Autonomous systems that can reason, make their own decisions, and execute actions across an environment introduce a category of risk that application-level controls were never built to contain. Treating that risk as a single problem produces incomplete architectures, says Oscar Wahlberg, senior director of product management at Nutanix. “The guardrails to catch …

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Cohere Parse 5 loses the benchmark on points. It wins on cost per page.

Enterprises trying to feed PDFs, slides and scanned documents into AI pipelines keep running into the same wall: the tools either miss the structure — tables, charts, layout — or cost too much to run at scale. Cohere released Parse 5 on Thursday, positioning it on price-to-performance, not raw accuracy — the right cost-capability mix …

Cohere Parse 5 loses the benchmark on points. It wins on cost per page. Read More »

Enterprise AI’s real risk isn’t autonomous agents. It’s the complexity between them.

Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it. That’s because enterprises don’t deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in …

Enterprise AI’s real risk isn’t autonomous agents. It’s the complexity between them. Read More »

When agents act on their own, governance has to live in the data layer

Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually …

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