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Field SOP

Scenario-based tutorials with real screenshots, prompts, workflow templates and pitfalls.

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Freeload OpenAI's Top Agent Foundation: The Codex Harness Integration SOP - a Three-Stage Rocket from One Command to a Production Runtime

The integration SOP for freeloading OpenAI's top agent foundation: a three-stage rocket. Stage 0 authentication (login_chatgpt / login_api_key; keys in env vars, never prompts) -> Stage 1 codex exec, one command for CI/scripts -> Stage 2 the Codex SDK as a callable function (startThread/run, outputSchema structured output, thread persistence in ~/.codex/sessions with resumeThread breakpoint resume, runStreamed event streams, sandbox and filesystem-granularity permission config examples) -> Stage 3 codex app-server as a production runtime (JSON-RPC 2.0, generate-json-schema, first-class Approvals semantics, -32001 overload retry, /readyz /healthz probes). Includes a 10-item pre-launch checklist and five classic pitfalls (keys in prompts, trusting sandbox defaults, non-Git directories, re-running instead of resuming, jumping to app-server on day one). All examples from the official repo docs; not legal or security-compliance advice.

Agent Guardrails SOP: If OpenAI Got Burned, Where Is Your Permission Checklist?

A deployment SOP for putting reins on AI agents: even OpenAI just crashed (its Aug 18 slowdown announcement), so ordinary teams need engineered guardrails all the more. Five steps: a three-question risk tier (L1 read-only / L2 sandboxed writes / L3 production, money, or external systems) -> least-privilege credentials (keys in env vars never in prompts, scoped tokens with expiry, physical separation of production and agent credentials) -> the sandbox execution layer (selection conclusions from this batch's comparison: E2B for fastest launch, CubeSandbox for self-hosted out-of-the-box, agent-sandbox on K8s - with a minimal E2B example) -> monitoring and approval gates (full behavioral logs, human approval for high-risk actions, and the cheap-model-watches-expensive-model AI-monitor pattern) -> circuit breakers and incident response (dual token-and-action caps, auto-pause on anomalies, a four-column post-mortem template). Includes a 10-item pre-launch checklist and five classic pitfalls. Not legal advice.

Before You Hand Your AI Agent a Wallet: A Grounded SOP from Sandboxed Budgets to Real Payments, with Three Circuit Breakers

An SOP for wiring payments into AI agents: five steps - a three-question scope check (90% of needs stop at quotas) -> the sandbox layer (prepaid isolation, the QPS/daily/per-request cap trio, read-only payment tools, full logging; 7-day graceful-degradation gate) -> choose a rail (the stripe/ai official repo MCP path vs the x402-fetch npm package) -> hands-on integration (read-only-first MCP JSON config plus wrapFetchWithPayment code) -> three circuit breakers (limits / allowlist / human approval) and a launch checklist. Five pitfalls: credentials in prompts, capless launches, limits without allowlists, skipping the sandbox, and forgetting refunds and reconciliation. Not legal or investment advice.

Give Your AI Content a Legal ID Card: A Labeling Compliance SOP (Explicit + Implicit Labels, Hands-On)

An AI content labeling compliance SOP: four hands-on steps under the labeling Measures + GB 45438 - a three-question scope check -> explicit labels (per-content-type table + platform declaration toggles) -> implicit labels (full c2patool install / manifest.json trainedAlgorithmicMedia / embed-and-verify commands) -> publish and keep records. Five pitfalls: platform re-compression strips metadata / label-stripping tools are unlawful and ineffective / do not hide explicit labels / watermarks are just one implicit form / write labeling duties into outsourcing contracts. Not legal advice.

Hands-on SOP: One npx Line to Run DeepSeek Harness - Install, Four Modes, Trajectory Replay, and Your First Plugin

A DeepSeek Harness quickstart SOP: environment prep -> one-line launch via npx @deepseek-ai/dsh web (127.0.0.1:3080) -> mode-selection mnemonic (work in Standard / orchestrate in Code / test models in Minimal / write plugins in Creator) -> trajectory replay & fork for debugging -> your first dsh-plugin. 5 pitfalls: no production on v0.1 / audit plugins first / budget tokens (peak-hour price hike) / two network chokepoints / spartan UI is by design. Per the official repo.