Wardgate is a security gateway that sits between AI agents and the outside world — isolating credentials for API calls, isolating SSH keys for remote command execution, and gating command execution in remote environments (conclaves).
Give your AI agents access to APIs, SSH keys, and shell tools – without giving them your credentials or trusting them with direct execution. — Read More
Recent Updates Page 13
The Agent Stack Bet
Peek under the hood of most “production agents” shipping today and you won’t find intelligence. You’ll find custom plumbing, fragile session logic, shared service accounts, and a security model held together by hope. This can be so much better.
If you’ve spent the last 18 months putting agents into production, you already know the models and tools have gotten dramatically better. You also know the problems that are still burning your on-call rotation are not problems you can prompt your way out of. We are running into a stack ceiling, and it is quietly creating a governance and reliability gap that the next generation of agentic systems cannot grow through.
Right now the industry is living with what I’d call excessive agency: autonomous systems given broad permissions to get things done, then left to discover – at runtime, in production – that a schema drifted, an API changed, or a downstream service started returning PII it wasn’t supposed to. Agents mark tasks “complete” while leaving a trail of corrupted state behind them. The humans find out on Monday.
This is not a failure of the people building agents. It is a failure of the stack they’re building on. — Read More
Mythos, Memory Loss, and the Part InfoSec Keeps Missing
InfoSec has a bad habit of acting like history started this morning. Something new lands, the industry loses its mind for a week, vendors start talking like the old rules no longer apply, and half the industry suddenly forgets how organizations actually get compromised.
We are doing that again with Mythos.
Mythos is legitimately impressive. It is very good at finding bugs, useful for exploit development, and materially improves the speed and quality of vulnerability research work. Anyone pretending otherwise is coping. But the conversation around it is already drifting into the same bad pattern this industry falls into every time a new offensive capability shows up: people fixate on the most technically dramatic part of the story and lose sight of what actually matters operationally.
That is the problem. The question is not whether Mythos is good at bug hunting and helping write exploits, it clearly is. The question is what that means for most defenders right now, and the answer is not “drop everything, autonomous zero-day machines are now the main thing compromising your environment.”
For most organizations, the bigger problem is still much more boring and damaging: ransomware crews, extortion operations, stolen credentials, phishing, exposed edge services, weak identity controls, stale appliances, known vulnerabilities, bad segmentation, and environments where once somebody gets in, they can move far too easily. Mythos does not replace that reality, it lands on top of it. If you miss that, you end up having the wrong conversation and spending your time talking about AI-generated zero-day storms while attackers keep getting paid through the same doors defenders left open last quarter. — Read More
The State of AI Adoption in the Enterprise [Q1 2026 Review]
You’ve seen the headline: “95% of enterprise AI pilots fail.”
… The 95% figure measures one thing: whether an AI pilot produced rapid P&L impact within six months. Not productivity. Not cost savings. Not efficiency gains. And it mostly measured pilots in sales and marketing — the lowest-ROI area in the study.
Measured that way, most projects will “fail.” A new hire doesn’t move the P&L in six months either… they often take six months or more to ramp up!
The study’s most important finding got buried: vendor-led deployments succeed 67% of the time. Internal builds succeed one-third of the time. This was always a story about strategy, not technology. This is a better takeaway for enterprises to focus on. — Read More
Salesforce launches Headless 360 to support agent-first enterprise workflows
Salesforce is packaging its developer and AI tooling, including its vibe coding environment Agentforce Vibes, into a new platform named Headless 360, designed to help enterprise teams build agent-first workflows.
The CRM software provider defines agent-first workflows as enterprise processes in which software agents, rather than human users, carry out tasks by directly invoking APIs, tools, and predefined business logic.
To support this approach, Headless 360 exposes Salesforce’s underlying data, workflows, and governance controls as APIs, MCP tools, and CLI commands, via its existing offerings, such as Data 360, Customer 360, and Agentforce, Joe Inzerillo, president of AI technology at Salesforce, said during a press briefing. — Read More
Why Agentic AI Is the #1 Skill To Learn
I’m not here to tell you AI is coming for your job. You’ve heard that a hundred times already, and frankly, nobody wants to here the same thing again.
You’ve also probably read the top skills to learn in 2026. Learn Python. Learn AI. Learn prompt engineering. Sure all those are valid. But here’s the thing: everyone is saying that. And when everyone is saying the same thing, the real opportunity is usually one step ahead.
So what’s that step?
Agentic AI. And hang on, it’s not some buzzword to add to your LinkedIn bio. It’s a fundamental shift in what AI does, how it thinks, how it works, and what it’s capable of. Right now, very few people understand it deeply enough to actually build with it.
That gap is exactly where opportunity lives. — Read More
Don’t choose the WRONG career in 2026 (Data Scientist vs. ML Engineer vs. AI Engineer)
π0.7: a Steerable Model with Emergent Capabilities
We’ve trained a new model, π0.7, that exhibits a step-change in generalization. π0.7 is a general-purpose model that can perform a wide range of dexterous tasks with the same performance as fine-tuned specialists, but even more importantly, it can follow new language commands and perform tasks that were never seen in its training data. In our experiments, we see π0.7 exhibiting the first signs of compositional generalization, recombining skills from various tasks to solve new problems, like using new kitchen appliances and even enabling a new robot to fold laundry for which there is no laundry folding data.
… A true generalist model should perform all of the skills out of the box, and be able to recombine them to solve new tasks. π0.7 demonstrates initial signs of such general capability: it can perform dexterous manipulation skills like those we’ve previously shown with our RL fine-tuned π*0.6 specialist models, with the same speed and robustness, it can compose and recombine the skills it learned to solve new tasks, and it can generalize across robot platforms, scenes, and tasks more effectively than our prior models. The examples below illustrate this breadth of capability, from fine manipulation to long-horizon household behaviors all with one model, straight out of the box. — Read More
What Is Vibe Engineering? How AI Turns Ideas Into Working Prototypes Instantly
For most people, ideas used to die before they were ever built.
… “How are you actually going to build this?”
And we didn’t have a real answer.
Fast forward to today, that exact situation looks very different.
If you have an idea now, you don’t immediately worry about whether you can build it or not. You open an AI tool, start describing what you want, explore possibilities, and within minutes, you have something that resembles a working prototype. The barrier between imagination and execution has almost disappeared.
This shift is what we call vibe engineering. — Read More
UK gov’s Mythos AI tests help separate cybersecurity threat from hype
Last week, Anthropic announced it was restricting the initial release of its Mythos Preview model to “a limited group of critical industry partners,” giving them time to prepare for a model that it said is “strikingly capable at computer security tasks.” Now, the UK government’s AI Security Institute (AISI) has published an initial evaluation of the model’s cyberattack capabilities that adds some independent public verification to those Anthropic reports.
AISI’s findings show that Mythos isn’t significantly different from other recent frontier models in tests of individual cybersecurity-related tasks. But Mythos could set itself apart from previous models through its ability to effectively chain these tasks into the multistep series of attacks necessary to fully infiltrate some systems. — Read More
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