OpenClaw Academy
AI EngineeringProduction agentic AI engineering — from OpenClaw to autonomous systems. Agentic AI from scratch to frontier · for every engineering role. A practitioner-first curriculum covering AI agents and the OpenClaw platform. Real working code every post.
| Platform |  | Pricing | Freemium | Publishes | Daily |
|---|---|---|---|---|---|
| Issues | 34 | Founded | 4 months ago | Last Issue | 7 days ago |
| Active | |||||
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openclawacademy.substack.comLatest Issues
Incident Response for Agents: When the Loop Goes Wrong
An agent incident is not the same as a service incident. A service goes down or returns errors — the failure is obvious and the blast radius is bounded by what the service was doing when it failed. An agent can fail in ways that look like s...
Cost Engineering Deep Dive: Caching, Batching, Model Routing
OpenClaw Academy · Part 4, Issue 30
Issue 29 named the four cost levers: context size, model selection per task, prompt caching, and batching. This issue implements all four in a single deployable configuration that typically reduces API c...
Cost Engineering: The Right Model for the Right Task
Most engineers who see unexpectedly high API costs make the same diagnostic mistake: they assume the solution is a cheaper model. Sometimes it is. More often, the solution is a smaller context for the same model.
The cost of an LLM call is...
Observability Stack: Prometheus, Grafana, Cost Attribution
OpenClaw Academy · Part 4, Issue 28
Issue 27 named the three observability gaps unique to agents: intent vs. execution drift, emergent multi-agent behaviour, and cost attribution per skill. This issue implements the observability stack tha...
Observability: Three Gaps Unique to Agents
Datadog, Prometheus, and New Relic were designed for request-response systems. A request arrives. A response is sent. The request either succeeded (2xx) or failed (4xx, 5xx). Metrics, logs, and traces map cleanly onto this model.
Agents br...
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