Introverted EMs; Wardley Maps for Tech Leads; Coding with Agents; Micromanagement Stigma in Startups
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Advantages of Introverted Engineering Managers
Ever wonder if your introverted nature is a superpower in the world of engineering management? This blog from Alex Ponomarev dives deep into how your quiet strengths, from understanding your team on a deeper level to excelling under pressure, actually give you a unique edge.
If you're an introverted engineer, or an aspiring EM, you'll find out how your natural tendencies can help you create the ideal environment for your team, manage effectively from the background, and master the art of listening and reflecting. This isn't just about surviving as an EM; it's about thriving and genuinely connecting with your team.
Ready to see how your introversion can make you an exceptional engineering manager? You won't want to miss the full read!👇
Article of the Week ⭐
“Imagine you're navigating a complex open world video game like GTA. You wouldn't just wander aimlessly, right? You'd want a map. That's kind of what a Wardley Map is for your business and tech strategy.“ —Alex Ewerlöf
Wardley Maps & Pace Layering for Engineering Leaders
Alex Ewerlöf offers two practical strategy tools: Wardley Maps and Pace Layering to help senior engineers and tech leads make more aligned, resource-savvy decisions. It’s aimed squarely at those wrestling with the challenge of connecting tech choices to business priorities, communicating architectural direction, and focusing limited bandwidth where it truly matters.
Understand where tech lives in your value chain
Wardley Maps start with user needs and show the value chain vertically (Y-axis) and the evolution of each component horizontally (X-axis). By placing systems like UIs, APIs, and infra into stages like Genesis, Bespoke, Product, and Commodity, leaders gain clarity on where to innovate, where to consolidate, and where to sunset. Mapping helps spot areas of tech debt, under-leveraged market solutions, and over-engineered components. It also frames better arguments for where investment (time, headcount, budget) will deliver compounding benefits.
This can visualise early decisions points, for example:
identifying whether your authentication stack should be maintained as a homegrown solution or migrated to a widely adopted platform.
Mature, commoditized components require higher reliability and SLAs
Early-stage ones need tolerance for volatility and lower SLOs.
Next up: Pace Layering
While Wardley Maps are driven by market evolution, Pace Layering is driven by business needs. The model splits systems into three categories:
Systems of Innovation (SOI): Fast-moving, experimental projects.
Systems of Differentiation (SOD): Core product logic with medium change rates.
Systems of Record (SOR): Slow-changing, high-reliability backbones like payroll or customer data.
This lens supports decisions about governance, architecture seams, deployment cadence, and resourcing. It also justifies why CI/CD fits some teams and not others.
Both tools for layered insight
Wardley Maps give external strategic awareness. Pace Layering supports internal architectural and team governance. For instance, a System of Record like an ERP system may be “Commodity” in a Wardley Map but managed as slow-changing in a Pace Layer to preserve data integrity. By combining both, engineering leaders can advocate for decisions that align with long-term business health while optimizing short-term execution.
Lightweight entry points and related frameworks
If you’re not sure where to begin, keep it simple:
Map a user story
Categorize a new feature by pace,
And use the discussion to guide budget or team conversations
These concepts have a wide overlap with Team Topologies, Flow Engineering, Event Modeling, Domain-Driven Design (for boundary clarity), and City Maps (for high-level systems visibility).
Other highlights 👇
How I program with Agents
With access to bash, git, grep, and a test suite, agents can move through real codebases editing files, running compilers, inspecting errors, patching tests, even updating CSS after seeing a screenshot. David Crawshaw describes building GitHub App auth in sketch.dev with only a few prompt refinements and catching major security and performance issues mid-process.
What makes it work
Agents benefit from structured feedback: compiler errors, failing tests, API docs, logs, rendered UI.
The feedback loop turns an LLM from a code generator into a code editor that adapts to its environment. The resulting workflow is slower than a direct LLM call, but often dramatically more useful. Running agents inside containers solves two major problems:
safety (they don’t touch your local credentials)
parallelism (you can spin up multiple at once)
This isolation unlocks workflows like writing code in one container while fixing UI in another.
Are IDE’s dead?
The IDE is changing. Diff views are now editable, review comments double as agent instructions, and SSH access to agent containers lets you jump in when you need to. Code review workflows, IDE assumptions, and team habits are all in flux. When coding with agents, the IDE quickly loses its “integration”. Agents allow interfaces like code review, via gitops or straight through a prompt with a headless checked out repository.
The underlying models are finally ready. With agents, programming becomes more about steering than typing and the systems we use to support that are only just catching up. They still require plenty of guard rails to keep them from taking over your machine, introducing another vector for supply chain attacks and social engineering.
Is All Micromanagement Bad?
The conventional wisdom says to avoid micromanaging. But in practice, many respected startup leaders have found value in getting closer to the work when done with purpose. Startup leaders from Stripe, Rippling, Apple, and more outline how selective involvement improves execution, team trust, and product quality.
What the best leaders actually do:
Jack Altman (Lattice) uses targeted micromanagement to model quality and direct attention. It’s a tool for standard-setting, to be in rare, critical instances.
Matt MacInnis (Rippling) relies on the principle of go and see when the metrics don't tell the full story. He digs into calls, tickets, and code when needed, then steps back once metrics improve.
Krithika Shankarraman (Stripe, OpenAI) built consistent review systems using 20%/80% checkpoints and “red pen holders” to scale decision quality without central control.
Mike Brown (Uber, Newfront) schedules quarterly deep dives into KPIs tied to growth, efficiency, and customer experience. He increases involvement when urgency or risk is high.
Systems that prevent unproductive micromanagement:
Jay Desai (PatientPing) flags micromanagement as a signal of reduced trust. His playbook includes diagnosing root causes early and rebuilding clarity before friction escalates.
Sidharth Kakkar (Subscript) uses a monthly feedback loop to reduce misalignment and increase accountability in a meeting-light, remote-first environment.
Hareem Mannan (Squint, Segment) delegates feedback through peer office hours and design critiques. Structured social time builds the trust needed for peer reviews to work.
Michael Lopp (Apple) encourages leaders to give context through narrative and example, letting teams choose how to apply it.
Sam Corcos (Levels) now hires managers who can perform the work themselves. This helps reduce reliance on escalation and keeps decision-making distributed.
Find Yourself 🌻
That’s it for Today!
Whether you’re innovating on new projects, staying ahead of tech trends, or taking a strategic pause to recharge, may your day be as impactful and inspiring as your leadership.
See you next week(end), Ciao 👋
Credits 🙏
Curators - Diligently curated by our community members Denis & Kovid
Featured Authors - Alex Ponomarev, Alex Ewerlöf, David Crawshaw, First Round
Sponsors - This newsletter is sponsored by Typo AI - Ship reliable software faster.
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