AI Agents
Everything related to agentic flows, harnesses, development with agents, building agents.
Seat vs usage pricing, editor lock-in, enterprise controls, policy commitments and a trial design that measures outcome, for a twenty-engineer team.
How to cap what an agent can spend per task: enforce at the call site, price tokens by class, define breach behaviour, and charge retries to the same ledger.
How resources an AI agent provisions expire by default: Cloudinary's 24-hour claim window, what claiming requires, and what shares the deadline.
How to add approval steps to an autonomous agent: persist state first, batch requests, show diffs, expire safely, and record who approved what.
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.
Agent-provisioned Cloudinary environments lock delivery to one public IP. Uploads succeed, images 404 in the browser. Symptoms, checks and fixes.
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.
Cloudinary's unauthenticated agent account-creation endpoint: what it returns, why email verification gates it, and when to use it.
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.
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.
How to keep a fan-out agent inside provider rate limits: bounded worker pools, both limit axes, Retry-After handling, and jittered retries.
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.
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.
Diagnose and fix egress control gaps in agent sandboxes: exfiltration paths, DNS side channels, credential blast radius, and in-process policy bypass.
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.
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.
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.
What model routing does, what the decision costs, why heuristic routing beats classifier routing at small margins, and why fallback routing comes first.
Billing shape, duration ceilings, warm state, cold starts and concurrency limits compared for agent runtimes — with a per-condition recommendation.
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.
Containers, microVMs, WebAssembly and hosted sandboxes compared on startup latency, blast radius, credential exposure and operational cost.