InfoWorld AI·
Application programming interfaces have been successful because they define the limits of permissible exchange, including who may take what action, when, and under what circumstances. Those limitations create a framework for understanding the behavior of distributed systems. And they make it possible to enforce policy at the boundary between interacting systems. What constrains distributed systems isn’t access, but execution. With autonomous data movement and action occurring at machine speeds, where processes unfold sequentially over time rather than as a singular event, APIs no longer provide a sufficient means of enforcing boundaries. The problem is no longer whether a request is valid. It is whether a sequence of actions remains safe. For agentic systems, there needs to be runtime guardrails around what they can read, write, and execute. Microsegmentation, enforced through network and kernel-level policies, defines those guardrails. APIs made systems predictable APIs were successfu
Read full articleWith the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architecture—the…
Salesforce's European AI expansion could redefine enterprise tech, raising critical issues around data ownership and transparency in AI systems. The post Salesforce pours billions into European AI expansion as agentic systems reshape enterprise tech appeared first on Crypto Briefing.
I've been spending a lot of time thinking about this, and more importantly, living it while building agentic systems at 2Q AI. What follows are real incidents, real architectural breakdowns, and a practical framework for keeping your AI agents from going off the rails...
Amazon Web Services has launched a dedicated internal organization of forward-deployed AI engineers, committing $1 billion in internal resources to help enterprise customers move beyond AI experimentation and into operational deployment. The new group will embed AWS engineers directly within client organizations to build and install purpose-built agentic systems, with an emphasis on speed and […]
"Agent" is the most overused word in AI right now. But strip away the hype and what are you actually working with? Adobe principal scientist Deepak Pai breaks down the real building blocks of agentic systems and when they're worth reaching for.
Workday is aiming to help customers to develop and deploy agentic systems without compromising corporate security or compliance, unveiling a series of AI tools at its DevCon event this week. Chief among them is Agent Passport, which validates an agent’s safety and compliance both before it is deployed, and continuously during its operation. When an agent attempts a task, Agent Passport can allow, block, or route the action appropriately, and problem agents can be stopped or restricted, based on company policy. Agents will be vetted for a series of risks, including prompt injection, jailbreak and goal hijacking, system prompt extraction, leaks of employee data, and unsafe outputs. Those tests will be tied to public standards such as Mitre ATLAS, and will be performed by security partners, not by Workday. Security teams can view those attestations, receiving a signed, auditable record of who tested the agent, and what it was tested for. Because every check is tied to a public standard, s
Over the years, enterprise IT execs have gotten frighteningly comfortable having little control or visibility over mission-critical apps, from SaaS to cloud and even cybersecurity. But generative AI (genAI) and agentic systems are taking that problem to a new extreme, with vendors able to dumb down a system IT is paying billions for without so much as a postcard. It’s not necessarily that AI changes are made to boost profits or revenue. Even if we accept the vendor argument that such changes are in the customer’s interest, companies still need for their systems to do on Thursday what they did on Tuesday, let alone what they did when the purchase order was signed. Alas, that is no longer the case. Consider a recent report from Anthropic that detailed a lengthy list of changes the company made to some of its AI offerings — including one that explicitly dumbed down answers — without asking or telling customers beforehand. The report describes various changes the Anthropic team made on t
Meta touts strong benchmarks but admits "performance gaps" in agentic and coding systems.
