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

Most agentic AI systems are built pattern by pattern, decision by decision, without any governing framework for how the agent should reason, act, recover from errors, or hand off work to other agents. Without structure, agent behavior is hard to predict, harder to debug, and nearly impossible to improve systematically. The problem compounds in multi-step workflows, where a bad decision early in a run affects every step that follows.

Agentic design patterns are reusable approaches for recurring problems in agentic system design. They help establish how an agent reasons before acting, how it evaluates its own outputs, how it selects and calls tools, how multiple agents divide responsibility, and when a human needs to be in the loop. Choosing the right pattern for a given task is what makes agent behavior predictable, debuggable, and composable as requirements grow.

This article offers a practical roadmap to understanding agentic AI design patterns. It explains why pattern selection is an architectural decision and then works through the core agentic design patterns used in production today. For each, it covers when the pattern fits, what trade-offs it carries, and how patterns layer together in real systems. — Read More

Companies building for agents often treat them as a bolt-on feature.

This is a mistake.

Agents today are more like a new form factor – an interaction layer that sits between your product and your users.

That means you need to build for agents as a primary surface, not an afterthought.

… We learned this the hard way and overhauled our AI architecture two times in the last year. Now, our agent and MCP have 6K+ daily active users.

Here are the golden rules of agent-first product engineering we learned along the way.

1. Let agents do everything users can
2. Meet agents at their level of abstraction
3. Front-load universal context
4. Writing skills is a human skill
5. Treat agents like real users

Read More

Coding agents working from code alone generate shallow hypotheses. Adding a research phase — arxiv papers, competing forks, other backends — produced 5 kernel fusions that made llama.cpp CPU inference 15% faster.

Coding agents generate better optimizations when they read papers and study competing projects before touching code. We added a literature search phase to the autoresearch / pi-autoresearch loop, pointed it at llama.cpp with 4 cloud VMs, and in ~3 hours it produced 5 optimizations that made flash attention text generation +15% faster on x86 and +5% faster on ARM (TinyLlama 1.1B). The full setup works with any project that has a benchmark and test suite. — Read More

The practices that make human pair programming effective—onboarding, structured design discussion, shared standards—apply equally to working with AI coding assistants. I propose five patterns that bring this collaborative scaffolding to AI-assisted development, shifting the experience from correcting a tool to collaborating with a capable teammate.

PATTERNS
Knowledge Priming
Design-First Collaboration
Context Anchoring
Encoding Team Standards
Feedback Flywheel

— Read More

Today, we’re launching Claude Managed Agents, a suite of composable APIs for building and deploying cloud-hosted agents at scale.

Until now, building agents meant spending development cycles on secure infrastructure, state management, permissioning, and reworking your agent loops for every model upgrade. Managed Agents pairs an agent harness tuned for performance with production infrastructure to go from prototype to launch in days rather than months.

Whether you’re building single-task runners or complex multi-agent pipelines, you can focus on the user experience, not the operational overhead. — Read More

SpecKit has 77,000 GitHub stars. AWS built an entire IDE around spec-driven development. Tessl raised $125 million on the promise that specs, not code, should be the source of truth.

The pitch was clean: stop vibe coding, write a proper specification, let the agent execute against it. Engineers loved it. It felt like rigor. It felt like the adults had finally entered the room.

Then someone actually tested it on a real project. Ten times slower. More ceremony. Same bugs.

The industry built an entire ecosystem around one idea: if we give AI agents a detailed enough spec, they’ll produce working software. It’s the same bet the industry made with outsourcing, with offshoring, with every model that tries to replace understanding with documentation. Write it down clearly enough and someone (or something) on the other side will execute it perfectly. —  Read More

Large language models (LLMs) have fixed knowledge, being trained at a specific point in time. Software engineering practices are fast paced and change often, where new libraries are launched every day and best practices evolve quickly.

This leaves a knowledge gap that language models can’t solve on their own. At Google DeepMind we see this in a few ways: our models don’t know about themselves when they’re trained, and they aren’t necessarily aware of subtle changes in best practices (like thought circulation) or SDK changes.

Many solutions exist, from web search tools to dedicated MCP services, but more recently, agent skills have surfaced as an extremely lightweight but potentially effective way to close this gap.

While there are strategies that we, as model builders, can implement, we wanted to explore what is possible for any SDK maintainer. Read on for what we did to build the Gemini API developer skill and the results it had on performance. — Read More

Last year, during an engagement with an insurance company, I worked with the product leadership team to understand why their 8-month AI initiative had stalled. They’d assembled a dedicated AI working group, ran three PI planning cycles where AI use cases were formally assigned to Release Trains, and produced a 21-slide deck explaining their AI strategy.

They had not shipped a single AI-powered feature.

The working group was waiting on the Q3 plan to be ratified before beginning experimentation. The Release Trains were waiting on the working group’s recommendations. The 21-slide deck was in review with the PMO.

This wasn’t negligence or laziness. This also wasn’t a technology problem. This was SAFe working exactly as designed. — Read More

Something strange is happening in software engineering right now.

Companies adopted AI to speed up code generation, and on the surface, it worked. AI can write syntax faster than any human ever could. It can generate boilerplate, suggest implementations, create tests, and even imitate design patterns in seconds.

That sounds like the beginning of the end for software engineering.

But that is not what is actually happening.

The real story is more interesting. — Read More