Practical applications of artificial intelligence, machine learning infrastructure, AI product development, and the business implications of AI adoption.
81 articles
The hard part isn’t the model. It’s retrieval, permissions, tool latency, and proof. Here’s how production teams build agentic RAG systems that can be inspected and trusted.
Agents fail in three boring ways: they overspend, they break policy, or they quietly get worse. The fix is an SRE-style stack: budgets, policy-as-code, eval gates, and replayable traces.
Teams stopped losing money on “agent demos” by treating agents like production systems: scoped tools, policy gates, eval suites, and cost-aware routing.
Single-call LLM features don’t survive contact with real workflows. In 2026, the differentiator is the system: routing, constraints, eval gates, and permissioned context.
Most agent failures don’t look like crashes—they look like plausible actions with ugly bills. Here’s the 2026 reliability stack: evals, policy gates, tracing, and cost ceilings.
If you can’t answer “what’s the maximum cost of one run?” you didn’t ship automation—you shipped a spend loophole with a chat UI.
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.
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.
If your “agent” can’t be replayed, scoped, and priced, it’s not a product. Here’s the 2026 enterprise standard: routing, contracts, SLOs, and audit trails.
Agents don’t fail because the model is “dumb.” They fail because nobody defined success, bounded actions, or built release gates. Here’s what production teams standardize in 2026.
Flashy agents fail the boring way: loops, bad tool calls, and quiet data damage. Here’s the production playbook teams use to ship agents you can audit, gate, and budget.
Agents fail the same way distributed systems fail: permissions, retries, and missing telemetry. Build the workflow first, then let models fill in the gaps.
Agents fail in expensive, quiet ways: extra tool calls, untraceable actions, and drift. Here’s the 2026 production stack teams use to ship workflows you can audit and control.
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