David Kim

The agent hype cycle is turning into an outage cycle. The winners in 2026 will treat LLMs like unreliable components inside deterministic systems—not autonomous coworkers.

David Kim

8 min read

The agent hype is real, but most teams are building privileged automation without the controls we already learned from cloud breaches. Fix the interface, not the prompt.

David Kim

8 min read

Most AI strategy decks fail because they dodge the only hard question: what changed, who approved it, and what breaks if it’s wrong?

David Kim

8 min read

The hard part of AI products isn’t prompts or models. It’s contracts: what the model may do, must never do, and how you prove it—every build.

David Kim

9 min read

Founders keep shipping “chatbots.” Winners are shipping testable AI systems with eval gates, traceability, and strict tool contracts.

David Kim

7 min read

In 2026, the startup wedge isn’t another chat UI. It’s proving your agent won’t leak data, break workflows, or rack up surprise bills.

David Kim

8 min read

LLMs moved decision-making into tools. If leaders don’t own the model layer—prompts, policies, and audit trails—culture becomes a black box and incidents become inevitable.

David Kim

8 min read

Founders keep treating fine-tuning as a product strategy. In 2026, the winners ship model-agnostic systems: retrieval you can audit, routing you can change, and contracts you can test.

David Kim

8 min read

By 2026, startups aren’t choosing a model—they’re inheriting a vendor maze. Here’s how to build an AI stack that won’t trap your product, margins, or roadmap.

David Kim

9 min read

The agent era isn’t a productivity hack. It breaks code review, CI, and ownership unless you rebuild the pipeline around specs, policies, and reproducible runs.

David Kim

8 min read

RAG demos still sell, but 2026 winners treat evaluation, provenance, and access control as the core system—not the model choice.

David Kim

7 min read

2026 product teams are done with demos. The winners are packaging agents as constrained workflows with auditable permissions, not free-form chat.

David Kim

9 min read

The winners won’t be the loudest copilots. They’ll be the teams that own identity, policy, audit, and cost control across models, tools, and data.

David Kim

9 min read

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

10 min read

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

10 min read

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

11 min read

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

10 min read

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

9 min read

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

10 min read

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

9 min read

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

9 min read

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

9 min read

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

8 min read

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

8 min read

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

10 min read

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

9 min read

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

10 min read

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

9 min read

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

9 min read

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

10 min read

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

10 min read

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

4 min read