> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cloudthinker.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Appendix B · The Agent Platform Engineering Checklist

> Forty scored items across eight surfaces for grading a vendor platform or reviewing an internal agent build.

*Forty items across eight surfaces. Score 0 (absent), 1 (partial), 2 (solid) — for a vendor evaluation or an internal build review alike.*

## Loop & economics (Ch. 1, 4)

* Per-task step, token, time, and spend budgets, with loop detection
* Deterministic-rails execution: models select typed actions; code executes
* Two-engine separation with a measurable promotion seam
* Cost per resolved incident reportable at reference scale
* Independent, state-based verification with soak windows and staged rollback

## Context (Ch. 2)

* Automated discovery producing a reviewable system map
* Change-stream ingestion, time-ordered and attributable
* Retrieval with re-ranking; context budgets; traceable compaction
* Memory with provenance, decay, contradiction flags, and a human-curated tier
* Knowledge and topology refresh with staleness handling

## Tools & skills (Ch. 3)

* Intent-shaped tools; structurally separated read vs. write paths
* Size-capped, structured tool results with pointers to full data
* Skills versioned in source control with owners, tests, and changelogs
* Skill metadata declaring access class, reversibility, and blast radius
* MCP supply chain: pinned versions, allowlists, contract tests, egress map

## Security (Ch. 5)

* Per-agent identity; short-lived scoped credentials; environment separation
* Taint-tagged context with delimited untrusted material
* Sandboxed execution (microVM-class) with no ambient credentials
* Default-deny egress with allowlists and novel-destination alerts
* PII/secret tokenization before every model boundary; guarded de-tokenization
* Injection detection in telemetry and a standing red-team cadence

## Policy & approvals (Ch. 6)

* Two-axis action classification, machine-derivable from skill metadata
* Versioned policy-as-code with review workflow, per environment and agent
* One-screen approval artifacts with structured rejection capture
* Pre-flight checks, staged application, circuit breakers, rate limits
* Pre-staged executable rollback on every consequential action; drilled kill switch

## Evaluation (Ch. 7)

* Golden set harvested from real incidents; replay harness
* Layered scoring incl. evidence integrity; state-based mitigation oracles
* Calibrated judges plus a standing human grading slice
* Regression gates on every prompt/tool/policy/model change, per class

## Observability (Ch. 8)

* One trace per incident: agent, step, tool, and model spans in an open schema (OTel GenAI)
* Fleet metrics: quality, latency, cost, drift, security signals
* SLOs wired to trust-ladder graduation criteria
* Customer-exportable traces and audit data

## Day 2 (Ch. 9)

* Versioned bundles (prompts, tools, skills, policy, model pins); no invisible change
* Model migration playbook with per-class diffs and canary lanes
* Memory hygiene cadence with named ownership
* Blameless agent postmortems feeding the golden set