Deep dives into software architecture, developer tools, programming best practices, databases, infrastructure, and the technical decisions that define modern software.
85 articles
Agents don’t fail because the model is “dumb.” They fail because teams skip contracts, budgets, and audit trails. Here’s the production playbook that holds up under load.
If your agents can write to real systems, you need more than prompts and tools. You need a control plane: identity, policy, budgets, traces, and eval gates.
Inference spend doesn’t scale like web requests. Treat AI features like real-time systems—budgeted, routed, traced—or your margins and SLOs collapse.
The agent outage isn’t a hallucination. It’s a tool loop that pounds your APIs, drags in the wrong data, and turns inference into an unbounded production dependency.
The hard part of agentic AI is letting software touch real systems without creating a security incident. Build agents like production services: scoped identity, policy gates, and replayable traces.
Chat UIs were the warm-up. The real 2026 stack is tool-gated agents, permissioned retrieval, and evals that catch failures before customers do.
If your agent can spend money or change systems, prompts aren’t guardrails. This 2026 stack focuses on controlled execution: typed tools, budgets, traces, and approvals.
If your agent can click buttons in Jira, Stripe, or GitHub, “good answers” don’t matter. What matters: permissions, traces, evals, rollbacks, and cost caps.
Copilots write drafts. Agents touch Jira, GitHub, and cloud APIs—and that forces you to treat prompts like code: permissioned, tested, observable, and budgeted.
Most agent failures aren’t model issues—they’re missing IAM, budgets, and replayable logs. Here’s the production checklist operators use to ship autonomy without chaos.
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.
Most “agents” fail for boring reasons: flaky tools, messy state, and missing approvals. Here’s the production stack teams use to ship automation without creating pager noise.
Agents don’t fail like apps. They fail like distributed workflows with fuzzy state—then leave no paper trail. Here’s how to build agents you can measure, cap, and audit.
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