VP of Engineering
David writes about engineering culture, team building, and leadership — the human side of building technology companies. With experience leading engineering at both remote-first and hybrid organizations, he brings a practical perspective on how to attract, retain, and develop top engineering talent. His writing on 1-on-1 meetings, remote management, and career frameworks has been shared by thousands of engineering leaders.
Agents fail in boring ways: stale context, untested changes, and overbroad permissions. The 2026 stack fixes that with MCP, eval gates, and tight blast-radius controls.
David Kim
Most agent failures aren’t “hallucinations.” They’re tool errors, runaway loops, permission mistakes, and unbounded spend. Here’s the production playbook that fixes those.
David Kim
The agent demo isn’t the product. The product is budgets, permissions, traces, and rollback—so customers can let automation run without babysitting it.
David Kim
If your “agent” can’t explain every tool call, cap spend per task, and fail safely, you didn’t ship a product—you shipped a demo.
David Kim
AI tools are everywhere. What’s rare is leadership that can let agents move fast without shipping nonsense, leaking data, or lighting money on fire.
David Kim
If your pitch is “we use the best model,” you’re already losing. In 2026, defensibility comes from workflow control, rights to feedback data, and distribution.
David Kim
Inference spend doesn’t scale like web requests. Treat AI features like real-time systems—budgeted, routed, traced—or your margins and SLOs collapse.
David Kim
Agent demos are cheap. Operating agents inside real systems of record isn’t. Ship one workflow with constraints, verification, and audit trails—or don’t ship it.
David Kim
Training is a project. Inference is rent. Here’s what operators change—routing, caching, batching, token budgets, and GPU utilization—so costs drop without wrecking UX.
David Kim
The hard part of agents isn’t tool wiring—it’s stopping bad actions, proving what happened, and keeping costs sane. Here’s the stack serious teams are standardizing on.
David Kim
Agents don’t fail like APIs—they “complete” the task while quietly doing the wrong thing. Here’s the 2026 reliability stack teams use to keep autonomy safe, cheap, and reviewable.
David Kim
Cool agent demos don’t pass security review. The 2026 winners ship audit trails, policy gates, and evals that survive model swaps and real permissions.
David Kim
A chatbot is a feature. An agent that moves work across systems—measured, traced, and permissioned—is the product. Here’s the 2026 stack and rollout.
David Kim
Agents can open PRs, change configs, and message customers. Leadership in 2026 is building boundaries, evals, and audit trails so speed doesn’t turn into incidents.
David Kim
Agents don’t fail like APIs—they take actions. Build them like operators: scoped identity, safe tools, deterministic guardrails, and traces you can replay.
David Kim
Static pipelines fail once LLMs can act. The teams pulling ahead treat traces, evals, and tool permissions as production systems—and ship changes behind gates.
David Kim
Chat demos don’t survive procurement. In 2026, the durable AI products are workflows with clear boundaries, observable behavior, and action you can approve—or undo.
David Kim
Agents don’t “answer.” They execute. That turns AI into a cost center, an identity problem, and an uptime problem—unless you build a real platform around it.
David Kim
Remote teams don’t fail from distance. They fail because decisions vanish into chat. Build culture by making work legible: written decisions, real docs, and deliberate rituals.
David Kim
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