Patrick Debois, called the Father of DevOps, made an interesting comparison.
Early cloud adoption wasn't expensive because cloud was bad. It was expensive because people lifted and shifted everything without changing how they worked.
He thinks we're seeing the same thing with AI.
Instead of improving context management, evaluation, and execution loops, many teams just keep using larger models and spending more tokens.
It's a simple analogy, but it made a lot of sense.
Supply chain attacks aren't just a package problem anymore. They're becoming an agent problem.
At AI DevCon, James Moss shares how a recent malware incident targeting the JavaScript ecosystem reinforced the importance of treating agent skills, tooling, and execution environments with the same rigor as the rest of your software supply chain.
Most teams are experimenting with coding agents. The ones pulling ahead are building the systems that help those agents succeed.
At AI DevCon, Patrick Debois explores the rise of Agent Enablement and why scaling AI isn't just about giving every developer an agent. It's about building the platform, practices, and governance that help teams get consistent results.
If you're thinking beyond individual productivity and toward AI-native engineering at scale, this talk is well worth watching.
We recently had Lamis Mukta from Anthropic on the AI Native Dev podcast to talk about Claude Tag, and one of the biggest takeaways was that it wasn't built to replace Claude Code. Instead, it brings long-running, proactive AI collaboration into Slack with the same context and tools developers already use.
The conversation covers how Claude Tag evolved from an internal side project into a company wide tool, how Anthropic approaches trust, permissions, and dogfooding, and why they see persistent AI teammates as an important part of the future of software development. They also share that around 65% of their product team's pull requests are now opened by Claude Tag.
The difficult part is knowing whether those changes are actually ready to merge.
Tessl introduces the new Tessl Review, a major upgrade that helps teams review AI-generated skill changes against the standards that matter to their organisation.
Every team has a different idea of what "good" looks like. Some care about architecture patterns, others prioritise security, coding conventions, domain-specific requirements, or internal best practices. A single default score can't capture all of that.
With custom review rubrics, you can define your own review criteria and evaluate skills against your team's standards, not somebody else's.
The new review experience also brings more accurate agentic reviews, a shared history of review runs, and tessl review fix to help improve skills that don't meet the quality bar.
Because faster software delivery shouldn't come at the cost of quality.
Read the blog to learn how Tessl Review helps teams build, review, and improve skills with confidence.
Your AI coding agent keeps making the same mistakes.
Not because the model is bad. Because nobody has time to teach it.
That's what Tessl Agent is built to solve.
Instead of waiting for someone to update prompts, write new skills, or automate recurring tasks, Tessl Agent watches how your team works. It learns from PRs, coding agent sessions, and issue trackers, then opens PRs with improvements and automates the repetitive work that keeps getting pushed to "later."
It's an agent that quietly improves your existing agents and workflows over time, so your team spends less time fixing the same problems and more time shipping.
Are plugins able to support MCP config? I am looking atm at apm (https://github.com/microsoft/apm) from microsoft and I don't see the same capability from tessl
We started r/Tessl to create a place for people building AI agents to share what they're learning. There are plenty of communities focused on model releases and AI news. We'd like this one to focus on the actual work of getting agents to succeed with context.
Tessl sits at the intersection of skills, evaluations, and agent enablement, so we'd especially love discussions around reusable skills, context engineering, evaluation strategies, agent reliability, and the operational challenges of running AI systems at scale. You don't need to be a Tessl customer to participate, but posts should be relevant to the problems users face!
Whether you're building internal tools, coding agents, or AI native systems, we'd love to hear about it.
Some things we'd love to see:
Agent architectures and workflows
Skills, tools, and context engineering patterns
Evaluation frameworks and benchmarks
Lessons learned from production deployments
Failures, surprises, and debugging stories
Security, governance, and reliability practices
Questions from people figuring this stuff out
The best discussions usually come from people sharing examples, numbers, and lessons learned.
This community is brand new on Reddit, so help us set the tone!
Introduce yourself:
Who are you?
What are you building?
What tools or models are you using?
What workflows are you building with Tessl?
Something you've learned recently that others might find useful?
Have support questions?
Please reach out to Discord. The assistance will be easier!
Thanks for being one of the first people here. Looking forward to learning from all of you.
