Senior AI Journalist
OpenAI Launches Workspace Agents: Enterprise AI That Works Across Slack, Salesforce and More
OpenAI has unveiled Workspace Agents, a significant successor to its Custom GPTs product, designed specifically for business teams. Unlike the previous generation which operated largely within ChatGPT’s own interface, Workspace Agents can plug directly into Slack, Google Drive, Microsoft apps, Salesforce, Notion, Atlassian Rovo, and dozens of other enterprise tools — and keep working even after the user has left the conversation.
The product is powered by Codex, OpenAI’s cloud-based AI coding harness, which gives agents the ability to write and run code, use connected apps, remember learned context, and continue work across multiple steps and days. A recurring reporting agent, for example, can pull data on a set schedule, generate charts, and share results with a team entirely autonomously. OpenAI is offering Workspace Agents free until May 6, after which credit-based pricing kicks in. More triggers, dashboards, and action capabilities are on the roadmap.
For enterprises, the architectural shift matters: building agents on a code-execution substrate rather than a pure LLM loop is what enables them to do real work — reconciling systems of record, transforming data, generating accurate charts — rather than just describing what the work would look like.
Source: VentureBeat
What This Means
This launch marks a critical evolution in enterprise AI. Custom GPTs, while innovative, were often limited by their sandboxed environment and reliance on direct user interaction. Workspace Agents, by integrating deeply with existing business applications and operating persistently, promise to move AI from a conversational assistant to an active participant in workflows. The underlying Codex architecture is key here, providing the agents with genuine agency to manipulate data and execute tasks, rather than merely generating text descriptions of actions. This shift is vital for achieving tangible ROI in complex enterprise environments where data accuracy and system reconciliation are paramount.
What to Watch
The adoption rate of Workspace Agents will depend heavily on the ease of integration and the robustness of the “no-code” or “low-code” configuration options for non-developers. Enterprises will also be keen to evaluate the security and data privacy implications of these agents accessing sensitive information across multiple platforms. Furthermore, the pricing model post-May 6 and the rollout of additional features like advanced triggers and dashboards will influence how quickly businesses scale their use of these agents. Keep an eye on how well these agents handle edge cases and unexpected data formats, as successful real-world deployment will hinge on their resilience and adaptability.
Gemini Comes to Air-Gapped Servers: Cirrascale Puts Google’s Flagship Model Fully Off-Cloud
Cirrascale Cloud Services, in partnership with Google Cloud, has launched what it calls the first fully private, disconnected deployment of Google’s Gemini model. Timed to Google Cloud Next 2026 in Las Vegas, the offering packages the full Gemini model — not a reduced version — into a Dell-manufactured, Google-certified hardware appliance with eight Nvidia GPUs, deployable inside customer facilities with zero internet connectivity.
The security architecture is striking: the Gemini model lives entirely in volatile memory, meaning it disappears the moment power is cut. User session data is cleared automatically when sessions end. If anyone attempts to tamper with the appliance, a confidential compute mechanism renders the machine inoperable and flags it for return. The offering enters preview now, with general availability expected in June or July 2026.
This addresses a longstanding blocker for regulated industries — banks, healthcare providers, government agencies — that needed frontier-class AI without surrendering data to third-party cloud infrastructure. The on-premises AI market is accelerating rapidly, and this is the clearest signal yet that even Google’s most advanced models are migrating out of hyperscaler data centres and into customers’ own racks.
Source: VentureBeat
What This Means
The availability of a full-scale Gemini model on air-gapped, on-premises hardware is a game-changer for highly regulated sectors. Previously, organizations in finance, healthcare, and government faced a difficult dilemma: leverage the power of cutting-edge AI models or maintain strict data sovereignty and security. This solution eliminates that compromise, allowing sensitive data to be processed by advanced AI without ever leaving the secure confines of the organization’s own infrastructure. The emphasis on volatile memory and tamper-proofing underscores a commitment to extreme security, which will build trust among these cautious enterprise clients.
What to Watch
The success of this offering will depend on its scalability and cost-effectiveness for various enterprise sizes. While the initial appliance serves a niche, the demand for secure, on-premises AI is broad. We should watch for how Google and Cirrascale expand this offering to include different hardware configurations and pricing tiers. Furthermore, the performance of the full Gemini model on a dedicated, localized appliance compared to its cloud counterpart will be a key metric for adoption. The market for “AI in a box” solutions is growing, and this move positions Google as a serious contender in the secure, on-premises AI space.
Startup BAND Raises $17M to Build the Communication Layer for Multi-Agent AI Systems
A new startup called BAND (Thenvoi AI Ltd.) has emerged from stealth with $17 million in seed funding, targeting one of the most underappreciated problems in enterprise AI: agents can’t talk to each other reliably. As businesses deploy fleets of AI agents built on different frameworks — LangChain, CrewAI, Salesforce, custom Python — these systems operate in isolation, unable to hand off tasks without brittle custom glue code.
BAND’s solution is an “agentic mesh” — a two-layer architecture that handles agent discovery, structured delegation, and multi-peer collaboration in real time. Critically, BAND does not use LLMs for routing (which would reintroduce the non-determinism it’s trying to solve) — it uses a patent-pending deterministic routing layer built on the same infrastructure stack as WhatsApp and Discord. The control plane enforces authority boundaries, managing which agents can communicate with which, and ensures human permissions traverse correctly across agent-to-agent delegations.
The product is framework-agnostic and cloud-agnostic, positioning BAND as neutral middleware in a market where hyperscalers are actively pushing enterprises toward their own ecosystems. As agentic AI moves from individual deployments to coordinated workforces, infrastructure for agent-to-agent communication is becoming a genuine category — and BAND is betting it can own that layer.
Source: VentureBeat
What This Means
BAND’s emergence highlights a growing pain point in the enterprise AI landscape: the fragmentation of agentic systems. As companies invest in various AI agent frameworks, the lack of a standardized, reliable communication layer severely limits their ability to collaborate and achieve complex tasks. BAND’s “agentic mesh” addresses this by providing a robust, deterministic routing mechanism that prioritizes reliability and security over the inherent non-determinism of LLM-based routing. This positions BAND as crucial infrastructure for the next wave of AI adoption, where coordinated agent teams will be essential for automating intricate business processes.
What to Watch
The success of BAND will hinge on its ability to truly remain framework-agnostic and provide seamless integration with the myriad of existing and emerging agent frameworks. Enterprises will also be keen to see how the “deterministic routing layer” performs under high load and with a diverse set of agent types and communication protocols. As the agentic AI market matures, the battle for the “middleware” layer will intensify, and BAND’s ability to demonstrate clear advantages in reliability, security, and ease of deployment will be critical for securing its position against potential offerings from hyperscalers or other startups.
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This article was produced with the assistance of AI tools and reviewed by the AIStackDigest editorial team.