Topics

Agents that drive a screen like a person do (click, type, read pixels) to use any app that has no API, and why that reach comes with the widest risk surface in agentics.

You’ll learn: Understand what computer-use agents actually do, why operating a GUI unlocks apps an API never will, where the reliability and safety problems concentrate, and which ClawBlog analyses to read next.

How you actually know an agent works: why multi-step agents resist evaluation, what to measure beyond a demo, and how tracing and reliability engineering close the gap to production.

You’ll learn: Understand why evaluating an agent is harder than scoring a model, what reliability actually means for multi-step work, which signals (traces, task success, cost-per-success) matter, and which ClawBlog analyses to read next.

The open standard for connecting agents to tools and data: what MCP is, why a shared protocol matters more than any single integration, and where the trust questions sit.

You’ll learn: Understand what MCP standardizes, why "build the capability once, reuse it everywhere" reshapes the agent ecosystem, what an MCP server actually is, and the provenance discipline it still requires.

Coordinating many agents toward one goal: the heartbeat, budget, and ticket plumbing that keeps a swarm from drifting, overspending, or deadlocking, and where it still breaks.

You’ll learn: Understand what orchestration actually coordinates, the core primitives (heartbeats, budgets, ticket queues), where multi-agent systems deadlock or false-signal progress, and which ClawBlog analyses to read next.

What running agents actually costs, why multi-step loops are the runaway-bill pattern, and how to reason about agent labor as a budget line rather than a magic discount.

You’ll learn: Understand where agent cost actually accrues, why the agent loop is the classic runaway-spend pattern, the controls that bound it, and how to compare agent labor against the human work it replaces.

Where AI-agent compromise actually comes from (skills, credentials, instructions) and the controls that cut the most risk for the least friction.

You’ll learn: Understand where an agent's trust boundaries actually sit, why most compromise is supply-chain and not a model exploit, and which few controls (skill curation, scoped credentials, isolation) do the most work.

Agent SDK coverage: the developer libraries for building agents — OpenAI, Google, Anthropic, and the framework field (LangChain, CrewAI, AutoGen) — and how to choose between them.

You’ll learn: Understand what an agent SDK gives you, how the major options (OpenAI Agents SDK, Google ADK, Anthropic, LangChain, CrewAI, AutoGen) differ, what to weigh when choosing, and which ClawBlog analyses to read next.

Agent harness coverage: the runner-and-loop layer that turns a model into an agent that acts — and why the harness, not the model, often decides behavior, cost, and safety.

You’ll learn: Understand what an agent harness is, how the harness layer (not the model) shapes real-world behavior, cost, and safety, the main harnesses in play, and which ClawBlog analyses to read next.

Claude Managed Agents coverage: Anthropic's hosted agent infrastructure — what “managed” actually changes, who it is for, and the tradeoffs versus self-hosting.

You’ll learn: Understand what Claude Managed Agents takes off your plate versus self-hosting, who the early customers are, where the lock-in and data questions sit, and which ClawBlog analyses to read next.

Paperclip coverage: the multi-agent orchestration layer for “zero-human companies” — its heartbeat/budget/ticket model, what it is good at, and where the autonomy gets risky.

You’ll learn: Understand what Paperclip orchestrates, how its heartbeat, budget enforcement, and ticket queue actually work, where a "zero-human company" breaks in practice, and which ClawBlog analyses to read next.

Hermes-Agent coverage: the self-improving, persistent-memory agent from Nous Research — how its memory model works, where its five backends fit, and the operational tradeoffs.

You’ll learn: Understand what actually makes Hermes-Agent different (persistent memory plus self-improvement), which of its five backends fits your setup, where the operational risk concentrates, and which ClawBlog analyses are worth reading next.

OpenClaw framework coverage, skill ecosystem analysis, security incidents, and practical operating playbooks.

You’ll learn: Understand where OpenClaw fits in the agent stack, how its skill ecosystem works, where security risk concentrates, and which ClawBlog analyses are worth reading next.