AI Agents
Everything related to agentic flows, harnesses, development with agents, building agents.
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How to have an agent provision a Cloudinary environment mid-session with one npx command, store the credential in a file, and claim it before it expires.
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An IDE extension and an MCP server expose the same vendor operations to different callers. Which one is a team decision, and when to run both.
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How resources an AI agent provisions expire by default: Cloudinary's 24-hour claim window, what claiming requires, and what shares the deadline.
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Agent-provisioned Cloudinary environments lock delivery to one public IP. Uploads succeed, images 404 in the browser. Symptoms, checks and fixes.
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How credit metering behaves under agent-driven work: one credit spans three axes, the window is rolling 30 days, and the limit arrives as errors.
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Cloudinary's unauthenticated agent account-creation endpoint: what it returns, why email verification gates it, and when to use it.
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What a vendor skill pack is, how Cloudinary's installs and lets a team select skills, and why vendor-side versioning changes assistant behaviour without review.
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A five-step procedure for handling a secret an agent was just issued: local env file, verified ignore rule, no client bundle, no reliance on redaction.
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How to keep a fan-out agent inside provider rate limits: bounded worker pools, both limit axes, Retry-After handling, and jittered retries.
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How durable workflow engines persist run position so a crash resumes from the last completed step, what determinism costs, and when a queue is enough.
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How to attribute agent cost and latency to the task that caused it using per-task traces, per-call spans, and cache-aware token accounting.
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Diagnose and fix egress control gaps in agent sandboxes: exfiltration paths, DNS side channels, credential blast radius, and in-process policy bypass.
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Where a mid-tier model matches a frontier one, where it doesn't, and how to decide per task class instead of once for the whole team.
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How golden sets and model-as-judge compare on stability, coverage, drift and bias when scoring AI systems in CI — and which to gate releases on.
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How to measure a coding assistant's effect on an engineering team: what acceptance metrics miss, why DORA is safe to publish, and when to capture a baseline.
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What model routing does, what the decision costs, why heuristic routing beats classifier routing at small margins, and why fallback routing comes first.
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Billing shape, duration ceilings, warm state, cold starts and concurrency limits compared for agent runtimes — with a per-condition recommendation.
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A step-by-step guide to building a CI suite for AI systems: property assertions, pass-rate thresholds, and setting a tolerance your team will not ignore.
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Containers, microVMs, WebAssembly and hosted sandboxes compared on startup latency, blast radius, credential exposure and operational cost.