Deep dives into software architecture, developer tools, programming best practices, databases, infrastructure, and the technical decisions that define modern software.
85 articles
Most “agents” fail for boring reasons: runaway spend, brittle tools, and missing audit trails. Here’s the 2026 build-and-buy bar for autonomy you can govern.
Agents fail in production for boring reasons: permissions, eval gaps, missing logs, and runaway cost. Here’s the stack that turns “autonomous” into “auditable.”
Agentic AI isn’t a chat feature anymore. If your system can change records or move money, you need permissions, proofs, and cost controls—by design.
Agents don’t break because the model is weak. They break because you shipped tool access without gates, tests, and budgets—and production always collects the debt.
Most agent outages aren’t “model issues.” They’re bad permissions, missing policy checks, and zero cost controls. Here’s the operator view of shipping agents safely.
Most “agent failures” aren’t model failures—they’re missing timeouts, sloppy tool permissions, and zero replay. Here’s the 2026 stack teams use to ship agents you can audit and afford.
Agents ship fast and fail loudly. AgentOps is the unglamorous layer that keeps tool calls, permissions, and costs from turning into incidents.
Agents don’t fail like chatbots—they fail like distributed systems. Here’s the 2026 stack for tracing, evals, policy gates, and rollback-ready automation.
Agents don’t fail like APIs—they take actions. Build them like operators: scoped identity, safe tools, deterministic guardrails, and traces you can replay.
Demos are easy. Keeping tool-using agents safe, cheap, and explainable under real SLAs is the job. Here’s the stack teams actually build to do it.
The hard part of shipping agents isn’t the model. It’s permissions, eval gates, audit logs, and rollback—so the agent can act without breaking trust or budgets.
The hard part of agents isn’t clever prompting. It’s proving what happened, blocking unsafe actions, and keeping per-task costs predictable as models and policies change.
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.
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