The Levr Blog
Building software with humans and agents.
Field notes on AI-native project management, agentic QA, and building software with humans and agents on the same team.
How to build product teams in the age of AI
AI-assisted product development means software teams use coding models and agentic workflows to turn ideas into shipped changes faster than ever, while still keeping humans accountable for system design, safety, and adoption. The hard part is no longer just writing code. It is deciding what to build
What Is Tessl Agent and How Does It Improve Coding Agents?
Tessl Agent is a system designed to make coding agents better over time by finding repeated mistakes, recurring chores, and workflow gaps across pull requests, issue trackers, and agent session history. Instead of relying on more manual prompting or one-off harness tweaks, it identifies patterns and
What are MCP Apps and how do they work?
MCP Apps are interactive applications built on top of the Model Context Protocol that let a host such as Claude, ChatGPT, Codex, or Cursor render UI from an MCP server instead of showing only plain tool output. Unlike a basic MCP server that returns structured data or text, an MCP App can return a s
Harness engineering and the path to a software factory
Harness engineering is the practice of building the feedback loops, checks, and workflows that let coding agents do more of your software delivery work with less human correction. Instead of treating agents as one-off assistants, you treat them as parts of a system that can be improved over time. Th
What Is Agentic Software Development?
While agentic software development is rising, traditional human-centric tools bottleneck autonomous AI. To scale, teams need a dedicated control plane—a centralized system providing agents with shared context, live memory, and human-in-the-loop governance.
Designing AI Interfaces Beyond the Prompt Box
Prompting is still the dominant way people use modern AI, but it remains structurally similar to older batch-style computing. You package intent, submit it, wait, inspect the result, then try again if needed. That workflow feels natural only because it has become familiar, not because it is the best
The shipping sprint that exposed the real AI bottleneck
Vibe coding inside a large company is not just fast prototyping with AI. It is a temporary shift in how teams make decisions, share context, and move from idea to shipped software with fewer handoffs. In a month-long experiment at Automattic, small autonomous teams paused normal roadmap work and bui
Accept the risk, sandbox everything for AI security
Prompt injection is the reality that language-driven AI systems can be manipulated through inputs you cannot fully predict or sanitize. For teams building with coding agents, copilots, or internal AI tools, the practical response is not total prevention. It is containment. That means treating AI as
Claude Code Workflow: Give Your Agent a Project, Not a Prompt
A Claude Code workflow defines the infrastructure and context boundaries surrounding Anthropic's terminal-based AI coding assistant. While the model excels at generating and refactoring code within isolated sessions...
How malicious SKILL.md files can compromise AI coding agents
SKILL.md security is the practice of treating agent skills like software supply chain components, not harmless markdown snippets. A skill can contain instructions, references, downloads, and behaviors that push a coding agent to fetch files, read secrets, install packages, or send data elsewhere.
Why skills are the new code in agentic development
Skills in agentic software development are reusable pieces of context that tell coding agents how to act, what standards to follow, and how to complete recurring work safely and consistently. As development shifts from hand-writing every implementation detail to expressing intent, skills increasingl
What AI coding did to dev teams in 6 months
AI coding is changing software teams by shifting the hardest work away from typing code and toward specification, architectural judgment, verification, and operational supervision. Teams using tools like Claude Code, Cursor, and similar assistants often find that code arrives faster
Agent experience: redesigning dev platforms for AI agents
Agent experience, or AX, is the practice of designing software platforms so humans and coding agents can work together safely and efficiently. It goes beyond API compatibility. AX covers structured errors, machine-readable signals, event-driven workflows, clear system context, and approval paths...
Local LLMs: Your Fallback when Frontier Models Get Restricted
Local models are AI models that run entirely on your own hardware instead of a provider's servers. That means no API dependency, no per-token billing, and no risk that a policy change or outage suddenly removes access to a critical workflow.
WebMCP: making web apps faster and cheaper for coding agents
WebMCP is an emerging browser API that lets a web app expose structured tools to coding agents instead of forcing them to interpret screenshots, scrape the DOM, or guess selectors. In practice, that means agents can interact with the front end through an explicit contract defined by the page author,
The production AI playbook for enterprise agent deployment
Production AI transforms agent demos into resilient, measurable enterprise systems. Rather than starting with model selection, success requires defining business outcomes, building evaluation data, and tracing decisions before launch
Human in the loop: what to delegate vs. keep reviewing
Today, AI-native development is software delivery where coding agents handle much of the implementation, while humans define requirements, guide architecture, and review results. Like Level 4 autonomous driving, humans set the destination while staying ready to take control when needed.
Executable specs for agentic coding: how to build a verification layer
Executable specs turn product requirements, design constraints, and runtime behavior into machine-checkable inputs that coding agents can verify before code is merged. Instead of trusting generated code because it compiles, teams validate whether a feature matches intent, works in a real preview env
Securing AI Agents in Production: Prompt Injection, Sandbox, Supply Chain
AI security now extends beyond model misuse into software supply chains, prompt injection, sandboxing, least-privilege access, and the design of web systems that agents can safely operate. As coding agents move from autocomplete to execution, teams need better visibility, tighter controls, and...
tldraw and AI Agents: A Visual Approach to Coding
Agents on the canvas describes a way of using an infinite whiteboard as a shared workspace where humans and coding agents can see, interpret, and modify the same visual context. Instead of keeping AI inside a chat box, tools such as agent-first development platforms and canvas systems like tldraw tu