Dennis Traub

There are two debates happening right now: - CLI vs MCP - should agents call existing CLIs or use an MCP server? - API vs MCP - does wrapping a REST API in an MCP server add value, or just complexity? Both focus on how agents call tools. But what both aren't asking is, who holds the credentials when they do? I wrote about this angle here: dev.to/aws/missing-fr…

The hard part of AI integration isn't getting the agent to work. It's trusting it enough to look away. We're sitting there, watching the agent as it runs. Reading every response and double-checking every action. Not because it's failing, but because it's the only way to understand what it does. This isn't "integrating AI", it's babysitting it. It might do exactly what we want. Or hallucinate data. Or loop for 40 minutes, racking up a bill that makes us cry. But we don't know for sure until we check. And most of the time "check" means "watch it run". If we can't verify what the agent does without looking over its shoulder, we haven't integrated AI. We've hired a co-worker we don't trust. We're struggling to build autonomous agents, but what actually matters isn't more autonomy. It's less. Because constraining an agent isn't a compromise, it's the design principle that makes trust possible. And we already know how to do this. We've been defining permissions, establishing trust boundaries, verifying outputs, and building observability into our systems for years. The tools aren't new, we just haven't applied them to agents yet.

@ElizabethFue12 Right? That's exactly what I thought 😱

@dtraub Uuuu.. like an episode of Black Mirror

Not sure if I should be intrigued or scared...

Three small things that tripped me up when setting up a Python monorepo with uv workspaces - root naming, inter-package deps, and pytest collisions. dev.to/aws/3-things-i…

I think every dev is thinking about what the future of code is right now. Yesterday, AWS shipped an experimental Python library, AI functions which behave like standard Python functions, but are evaluated by reasoning AI Agents. Check it out! github.com/strands-labs/a…

Introducing Strands Labs 🧬🧪 github.com/strands-labs I think of Strands Labs as a playground for the next generation of ideas for AI agent development, from how to build agentic robots to how to make our everyday applications more agentic Come play with us 😉

@jeremy_daly This is great, thanks Jeremy!

Since I found myself with a bit more free time lately 😉, I decided to sit down and write out everything I’ve learned about building multi-tenant, commercial AI agent systems. I thought it would be a long blog post. A few days later, the Google Doc was well over 100 pages. Since late 2024, I’ve spent most of my time building and optimizing these systems with a team of engineers inside a large SaaS platform serving hundreds of enterprise customers. Multiple tenants. Material financial data. Hard requirements around isolation, auditability, retention, and cost control. When I wasn’t working on those systems, I was consulting with other teams, collaborating with peers building agent platforms, and running my own experiments to pressure-test orchestration models, retrieval architectures, evaluation harnesses, and model upgrades under similarly real-world conditions. What became obvious pretty quickly: Context isn’t just what you pass into a model. It’s infrastructure. It defines your isolation boundary, your cost surface, your audit trail, and your upgrade path. What I also noticed is how much of this is starting to converge across the ecosystem. Different stacks. Different abstractions. But once you introduce real production pressure, similar patterns start to emerge. This isn’t meant to be canon. It’s a distillation of the patterns I’ve seen actually hold up in production. Here is the guide: jeremydaly.com/context-engine… If you’re building in this space, I’d genuinely love your feedback.

When I wanted to add a local persistence layer to an agent I'm working on, my first instinct was to write it myself. But as it turns out, everything's already there! dev.to/aws/til-strand…

This is my third major technological shift, and every time I hear the same question echo through the C-Suites: "But where's the ROI?" In the 90s, we put our print brochures and yellow pages on the web - and called it digital transformation. In the 2000s, we put tiny keyboards or a Windows start button on a phone and called it mobile computing. Both times, the answer wasn't better brochures or smaller buttons. The answer was Google and Facebook. It was the iPhone and Android. New ideas, by companies that didn't try to replicate the old world - with all its constraints - on a new medium. They built something entirely new. They embraced the new medium and built what couldn't have existed before. And now we're doing it again, with AI. Every demo is a chatbot. Every pilot is "adding AI to our existing workflow." Every enterprise use case just tries to optimize what already exists. But this time, it kind of works. And that's the trap. The web's brochureware visibly broke. The stylus on Windows Mobile was physically painful. But AI chatbots deliver just enough value to feel like progress. We're settling for 10% of what's possible and call it "revolutionizing the [industry of your choice]". But the real inflection point isn't better support agents or travel booking chatbots. It's when we stop porting and start building.

An MCP tool silently dropped a request parameter and still returned "success" - 16 times - with 93K wasted tokens per attempt... If I hadn't realized something was wrong, I'd have paid ~$230 per month from a single bug. If your API accepts parameters it can't handle and returns a success response, you're not being graceful. You're being expensive. dev.to/aws/how-a-subt…

@trq212 What can possibly go wrong 😀

big week for Claude code desktop enjoyers coming up

Introducing Mistral AI's biggest hackathon ever! 📅 Feb 28 - Mar 1 🌍 Paris | London | NY | SF | Tokyo | Singapore | Sydney & online 48 hours. The best hackers. 🤝 Partners: @wandb @nvidia @awscloud @HackIterate 🏆 $200K in prizes. Special awards from @elevenlabs @huggingface @JUmp @whitecircle @supercell Link in 🧵

@ashtom @EntireHQ Nice, definitely need to have a look!

tl;dr Today, we’re announcing our new company @EntireHQ to build the next developer platform for agent–human collaboration. Open, scalable, independent, and backed by a $60M seed round. Plus, we are shipping Checkpoints to automatically capture agent context. In the last three months, the fundamental role of the software developer has been refactored. The incredible improvements from Anthropic, Google, and OpenAI on their latest models made coding agents so good, in many situations it’s easier now to prompt than to write code yourself. The terminal has become the new center of gravity on our computers again. The best engineers can run a dozen agents at once. Yet, we still depend on a software development lifecycle that makes code in files and folders the central artifact, in repositories and in pull requests. The concept of understanding and reviewing code is a dying paradigm. It’s going to be replaced by a workflow that starts with intent and ends with outcomes expressed in natural language, product and business metrics, as well as assertions to validate correctness. This is the purpose of our new company @EntireHQ, to build the world's next developer platform where agents and humans can collaborate, learn, and ship together. A platform that will be open, scalable, and independent for every developer, no matter which agent or model you use. Our vision is centered on three core components: 1) A Git-compatible database that unifies code, intent, constraints, and reasoning in a single version-controlled system. 2) A universal semantic reasoning layer that enables multi-agent coordination through the context graph. 3) An AI-native user interface that reinvents the software development lifecycle for agent–human collaboration. In pursuit of this vision, we’re proud to be backed by a $60M seed round led by @felicis, with support from @MadronaVentures, @m12VC, @BasisSet, @20vcFund, @CherryVentures, @picuscap, and @Global_Founders alongside a global group of builders and operators, including @GergelyOrosz, @theo, Jerry Yang, @oliveur, @garrytan, and many others, who all recognize that the time is now to take such a big swing. And we begin shipping today with Checkpoints, a new primitive that automatically captures agent context as first-class, versioned data in Git. When you commit code generated by an agent, Checkpoints captures the full session alongside the commit: the transcript, prompts, files touched, token usage, tool calls, and more. It’s our first crack at the semantic layer, as open source CLI on GitHub. From here on out, no more stealth. We are building in the open and as open source! More to come soon, in the meantime check out all the details in our blog.

@ujjwalscript How long did human programmers need to get this far?

Anthropic: Our AI agents coded the C compiler 💪🏼 The compiler:

We're securing our supply chain with SBOMs, signed images, pinned dependencies, adding vulnerability scans at every step... And now we're handing AI agents a registry of tools that can change their shape whenever they want. Every npm package gets audited, every Docker image gets signed, and every dependency gets pinned to a specific build. But what about MCP servers and tool registries? They can expand their offerings at any time, add new capabilities you never vetted, and widen their scope without a version bump. And here's what adds an entirely new kind of risk: the consumer isn't even deterministic. When your CI pipeline pulls a package, it does the same thing every time. When an agent picks a tool, it's making a judgment call - and that call can change with every single prompt. We built supply chain security because we learned the hard way: trust nothing you can't verify. Pin what you depend on. Know exactly what's running in your system. But AI tools don't work like that. They're dynamic. Discoverable. Designed to evolve. That's their power. But it's also their risk. A server you trusted for one function can offer ten more tomorrow. A tool that passed your review last month might behave differently now. And your agent - your non-deterministic, context-dependent, reasoning agent - gets to decide which tools to trust. Every tool an agent can call is effectively part of your software bill of materials. But unlike your other dependencies, you can't pin it. You can't sign it. You can't even be sure it's the same tool you originally approved. What does it mean for supply chain security when our dependencies can shape-shift - and our consumer decides on the fly which ones to trust?

"95% of companies see zero ROI from AI" You've seen this stat everywhere. Fortune. The Economist. FT. Board meetings. VC decks. It's attributed to "MIT research" so it must be valid, right? I spent months trying to verify it. Here's what I found 🧵 exponentialview.co/p/how-95-escap…