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RAG for Knowledge-Grounded Agents
An ungrounded agent is a confident liar. Ask it about your company’s refund policy, last quarter’s numbers, or a customer’s order history, and it will produce a fluent, plausible, well-structured answer — drawn from its training data, its priors, or nothing at all. The words are right; the facts may be invented. That’s the hallucination problem, and it’s the single biggest barrier between a demo and a system you’d let talk to customers. Retrieval-Augmented Generation (RAG) is the answer the industry converged on, and in 2026 it’s the default architecture for any agent that needs to answer from private or… Go to Post
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Free Booklet: Claude AI The Complete Guide
Anthropic ships major Claude updates almost weekly, which makes staying current with the platform a genuine full-time problem — even the best deep-dive guides go stale within weeks of publication. Claude AI: The Complete Guide solves that by treating currency as a feature: every pricing figure, benchmark, and model spec is timestamped, every correction from prior editions is called out explicitly rather than quietly fixed, and this August 2026 revision covers everything from the new Claude Opus 5 to the Fable 5/Mythos 5 export-control saga that briefly took two flagship models offline worldwide. Whether you’re writing your first API call… 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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AI Slop & Review at Scale
Sonar’s 2026 State of Code Developer Survey put a hard figure on a feeling every engineer already had: 96% of developers don’t fully trust that AI-generated code is functionally correct. That’s the stat that made headlines. But the more revealing one sits right next to it: only 48% always verify AI code before committing. Sit with that gap. Nearly everyone distrusts the output, and barely half consistently check it. Sonar calls the space between those numbers the verification gap — and AWS CTO Werner Vogels gave the accumulating consequence a name at re:Invent in December 2025: verification debt. The pressure… 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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Free Booklet: Monitoring And Auditing In AWS
Monitoring and Auditing in AWS is a practical guide designed to help developers, architects, and security professionals build robust visibility into their cloud environments. As organizations increasingly rely on AWS to power critical workloads, the ability to track activity, detect anomalies, and respond to potential threats becomes essential. This booklet explores the core services, patterns, and best practices that enable effective monitoring and auditing across AWS, providing a clear path to strengthening security posture, ensuring compliance, and maintaining operational resilience in modern cloud-native systems. Go to Post
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Free Booklet: Build And Deploy Anywhere With GPT-5 Codex
This interactive booklet is intended as an expanded version of the previous post. Due to the nature of code generation, the actual output and workflow will be different for each case. The code examples in this booklet are mainly for illustrative purposes. Go to Post
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Build and Deploy Anywhere with GPT-5.3-Codex
Software engineering has always evolved alongside its tools. Compilers turned human ideas into executable programs. Integrated development environments improved productivity and debugging. Version control systems enabled collaboration at scale. Continuous delivery pipelines made rapid and reliable deployment possible. In early 2026, another major step appeared: agentic coding systems capable of participating in the engineering process itself. One of the most advanced examples of this new class of tools is GPT-5.3-Codex, OpenAI’s latest coding-focused model designed to reason across repositories, plan multi-step changes, execute development tasks, and collaborate with engineers across the full software lifecycle. Unlike traditional autocomplete-style coding assistants, GPT-5.3-Codex… Go to Post
Professional Developer
by Rodrigo Silva
