Posts tagged "how-to"
What is a playbook for in agentic work?
A playbook makes a recurring agent task run the same way every time: fixed steps, typed handovers, checks and stop rules. How it relates to AGENTS.md, skills and runbooks.
Building an internal skills catalog: review, ownership and discovery
Build a skills catalog for your organisation: submit, review, publish with owner and version, then deprecate. Borrows ideas from public registries.
Ops series: how to deploy AI agents with canaries and rollbacks
Deploy AI agents like any service: send a new model, prompt or skill to a small share of runs first, let evals and SLOs decide and keep a rollback ready.
Structured output for agents: schemas, contracts and ownership
Structured output agents hand over typed data, not free text. Define JSON schemas for tool results and outputs, version them and name an owner per contract.
Ops series: LLM FinOps for agents, attributing every token
LLM FinOps applies the cloud playbook to agents: tag every run, attribute spend per key and team, set budgets, alert early and export usage to cost reports.
Enterprise AI agent rollout: pilot, baseline, then scale
An enterprise AI agent rollout works best with a small pilot, telemetry from day one, spend limits and written exit criteria for each wider step.
Self-hosted models for agents: what changes when inference is yours
Self-hosted LLM for agents: prompts stay in house, but you own capacity, updates and evaluation. Local runtimes versus batched servers and the ops work.
Ops series: turning runbooks into skills without losing control
Runbook automation AI done safely: turn a runbook into a skill plus narrowly scoped tools with approvals. A step-by-step conversion guide for ops teams.
LLM cost optimization for agents: caching, code execution and budgets
LLM cost optimization for agents: where prompt caching pays off, when code execution beats tool calls, and why hard per-run token budgets protect security too.
Claude Code hooks as policy enforcement points for coding agents
Claude Code hooks run code before a tool call, so a PreToolUse hook can enforce policy locally. How to write one, where it stops and why a central gate stays.
AI agent sandboxing: filesystem and network isolation together
AI agent sandboxing works only when file access and network egress are both limited. Why one boundary is not enough and how allow-lists cut approvals.
Versioning agent skills like any other dependency
Skill versioning treats agent skills as dependencies: semantic versions, changelog, pinning and evals that guard upgrades. What counts as a breaking change.
Draw the data flow before you write the first prompt
An agent data flow diagram shows where untrusted data enters, where private data lives and where anything leaves. One-page template for trifecta risks.
One MCP server per system: MCP server design you can govern
MCP server design rules for governable agents. One small server per system, namespaced tools, and read/write separation instead of one giant gateway.
Small verified steps beat one big prompt: incremental agent workflows
Incremental agent workflows break work into small steps with acceptance checks, progress notes and a clean state after each one. Here is the pattern.
Anatomy of an agent skill: SKILL.md, progressive disclosure and scope
SKILL.md is the entry point of an agent skill: its short description decides when the rest loads. Progressive disclosure, a minimal skill and scoping rules.
Writing tools agents can actually use: names, schemas and errors
Tool design for AI agents is interface design for a model reader. A checklist for names, namespaces, narrow schemas and errors, and how small tools ease policy.