Agentic AI has emerged as the software industry’s latest shiny thing. Beyond smarter chatbots, AI agents operate with increasing autonomy, making them poised to drive efficiency gains across enterprises.

“Agentic refers to AI systems that can take actions on behalf of users, not just generate text or answer questions,” says Andrew McNamara, director of applied machine learning at Shopify. Agentic systems run continuously until a task is complete, he adds, citing Shopify’s Sidekick, a proactive agent for merchants.

Development of agentic AI now spans many business domains. According to Anthropic, a provider of large language models (LLMs), AI agents are most commonly deployed in software engineering, accounting for roughly half of use cases, followed by back-office automation, marketing, sales, finance, and data analysis. — Read More

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

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

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

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

In complex, long-running agentic systems, maintaining alignment and coherent reasoning between agents requires careful design. In this second article of our series, we explore these challenges and the mechanisms we built to keep teams of agents working productively over long time spans. We present a range of complementary techniques that balance the conflicting requirements of continuity and creativity.

… Language model APIs are stateless: to provide continuity between requests, the caller must provide the complete message history with each request. Agent frameworks solve the state management problem for users by accumulating message history between API calls. This fills the agent’s context window, which provides a hard limit on how much information the agent can handle. Even approaching an agent’s context window limit can degrade the quality of responses. For short-run applications, no extra context window management is typically required.

Complex security investigations can span hundreds of inference requests and generate megabytes of output, requiring special handling. Multi-agent applications, like ours, add further complexities. For each agent to optimally execute its role, it requires a tailored view of the investigation state. Each view must be carefully balanced. If agents are not anchored to the wider team, the investigation will be disconnected and incoherent. Conversely, sharing too much information stifles creativity and encourages confirmation bias.

Our solution uses three complementary context channels: Director’s Journal, Critic’s review and Critic’s Timeline. — Read More

Earlier Article

Back in October, my AI assistant stored a memory with an importance score of 8/10. Content: “Investigating Bun.js as a potential runtime swap.”

I never actually switched to Bun. To be fair, it was a two-day curiosity that went nowhere. But this memory persisted for six months, popping up each time I asked about my build process and quietly pushing the AI toward a Bun solution with confidence.

There was nothing wrong with the system; it was doing exactly what it was supposed to do. That was the issue. — Read More

Skills have become one of the most used extension points in agents. They’re flexible, easy to make, and simple to distribute.XXXXBut this flexibility also makes it hard to know what good and what works. What type of skills are worth making? What’s the secret to writing a good skill? When do you share them with others?

I have been using skills extensively with many of them in active use. Here are some tips I’ve learned along the way. — Read More

Most AI agents fail in production not because the model is bad, but because keeping them running reliably costs months of engineering work that has nothing to do with the actual agent. Sandboxed containers, credential handling, state management, error recovery, all of it falls on your team before a single user ever sees the thing.

On April 8, 2026, Anthropic launched Claude Managed Agents in public beta, and the core pitch is simple: they handle that infrastructure layer, you handle the agent logic. — Read More

A beginner-friendly guide for developers who want to actually understand what they are building.

… There are numerous terms:

LLM, agents, vector databases, tokens, embeddings, RAG, and fine-tuning
Additionally, the majority of tutorials skip over the basics and start building chatbots right away.

The truth is simple:

AI becomes much easier once you understand the core concepts. — Read More