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Why Agentic Projects Fail
Almost everything written about agentic AI is a showroom: the demo that dazzled, the workflow that now runs itself. This article is the morgue. Because the most useful thing you can study before building an agentic system isn’t the success stories — it’s the autopsies. And there are a lot of bodies. The headline number is Gartner’s, from June 2025: over 40% of agentic AI projects will be cancelled by the end of 2027 — not scaled back, not pivoted, cancelled — citing escalating costs, unclear business value, and inadequate risk controls. It’s not a speculative warning; Gartner frames it… Go to Post
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Multi-Agent Orchestration: From One Agent to Many (and When Not To)
You start with one coding assistant and it’s great. So you add a second agent to review the first. Then a planner. Then a tester. Each addition feels reasonable, and somewhere around the fourth one the system starts to wobble: the logs balloon, the agents make contradictory decisions, costs spike, and you’re back babysitting it at 2 a.m. wondering whether all this orchestration actually bought you anything. That tension — more power versus more pain — produced one of 2025’s sharpest engineering disagreements. On June 12, Cognition (the team behind Devin) published “Don’t Build Multi-Agents,” arguing that parallel subagents make… Go to Post
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How Can AI Help My Business?
If you run a small business and you’ve been wondering whether this whole AI thing is for you, here’s the honest state of play in 2026: most of your peers have already started. Depending on which survey you read, somewhere between 82% and 89% of small businesses now use AI in some form — up from roughly a third just three years ago. The U.S. Chamber of Commerce found small firms adopting AI faster than large companies for the first time in the data’s history. But that headline hides the part that should reassure you. When researchers look at who’s… Go to Post
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The Senior Developer Bar in 2026
A lot of my posts are about how to build with AI — agents, orchestration, the Microsoft stack, the protocols. This series steps up a level and asks a harder question aimed squarely at senior and lead developers: in mid-2026, what actually makes you valuable? Because the answer has moved, and a lot of people haven’t noticed. The bar is no longer “I integrated an LLM” Two years ago, wiring an LLM into a product was a differentiator. You could stand up in a review, show a feature that called a model, and that was the value. In mid-2026 that’s… Go to Post
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Free Mini-Course: FND-101 AI Foundations for Practitioners
Modern AI has its own concepts, vocabulary, and ways of working that are unfamiliar even to experienced engineers. This course is the technical front door to the catalogue: it gives practitioners the grounding they need before entering any of the intermediate and advanced tracks, so they are not learning fundamentals and specifics at the same time.Students build an accurate mental model of what today’s AI is, how language models behave, what the application landscape looks like, and how quality and responsibility are handled. The emphasis is on genuine understanding rather than hype or hand-waving.Labs are gentle, guided exercises that let… Go to Post
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Context engineering as a software discipline
You ask your coding agent to add a new endpoint. It churns for a minute and produces beautiful, idiomatic code that compiles on the first try. You merge it. Then you deploy, and three services fall over. The agent ignored your authentication layer, bypassed the validation patterns every other handler uses, and pulled in a dependency that conflicts with your stack. None of that was in your prompt. You assumed the agent would just know — it’s been trained on millions of repositories, after all. But it had never seen your repository, your conventions, or your production constraints. The prompt… Go to Post
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CLI Agents vs AI-Native IDEs: The Token Economics
Here is the benchmark that’s been driving architecture decisions across the industry in 2026: a typical CLI command costs an agent around 200 tokens. The equivalent operation through an MCP server costs 32,000 to 82,000 tokens. That’s not a typo, and it’s not cherry-picked. Independent benchmarks from Scalekit, Apideck, and others keep landing in the same range — roughly a 35× overhead for MCP on identical tasks. When “MCP is dead. Long live the CLI” hit the top of Hacker News and Perplexity’s CTO publicly described moving away from MCP internally over context waste, they were all pointing at the… Go to Post
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MCP vs A2A: Two Protocols, Two Different Problems
If you’ve built anything with AI agents recently, you’ve heard two acronyms thrown around constantly, often with wildly conflicting takes: “MCP is the USB-C of AI.” “A2A replaces MCP.” “You need both.” “Neither is production-ready.” Most of that noise comes from a single confusion. MCP and A2A solve completely different problems, and treating them as competitors — or worse, as interchangeable — is one of the most common and most expensive architecture mistakes in the space right now. Get the distinction wrong and your system fights you at every layer: you’ll find yourself wrapping databases as “agents,” building bespoke RPC… Go to Post
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Agentic Engineering: From Vibe Coding to a Plan, Execute, Verify Discipline
For about a year, “vibe coding” was the most fun anyone had had with a keyboard. You describe what you want, an agent writes it, you skim the result, you ship. Andrej Karpathy’s original framing was almost gleeful: you “give in to the vibes” and barely read the diffs. Then teams tried to put vibe-coded software into production, and the bill came due. The failure mode now has a name — AI slop: code that looks reasonable on the surface but lacks error handling, quietly introduces security vulnerabilities, breaks something three modules over, or produces an architecture nobody can maintain.… Go to Post
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Eyes Everywhere: Secure Logging and Alerting for Modern Systems – Part III
Logging and alerting become truly powerful only when they are embedded into a well-designed security architecture. Individual applications emitting logs or isolated detection rules provide only partial visibility. Modern organisations operate distributed systems composed of microservices, cloud infrastructure, container platforms, and external APIs. In such environments, security visibility requires a unified architecture capable of collecting, analysing, and responding to telemetry across the entire system. Designing Systems That Detect and Respond to Threats A secure observability architecture combines several layers. Applications generate structured logs. Infrastructure produces telemetry about hosts, containers, and network activity. These signals are collected and aggregated through centralized… Go to Post
Professional Developer
by Rodrigo Silva
