Plan your agent.
Know what to check.
Start with architecture decisions and a worked cost exercise. Our cloud workflows are planning references; they are not yet end-to-end, tested deployment tutorials.
OFFICIAL SOURCES · HONEST LIMITS · NO INVENTED BENCHMARKS
ILLUSTRATIVE ARCHITECTURE, NOT A LIVE SERVICE STATUS.
Start with decisions you can check.
Where should your AI agent run?
Choose between an API-backed agent server and self-hosted inference by examining workload, data boundaries, hardware, and operational responsibility.
Planning referenceBudget an AI agent: a worked 30/90-day exercise
Follow a fictional API-agent budget from token usage to six calculator inputs, check normal and failure totals, and choose resources from evidence.
Worked budgeting exerciseAn operations checklist for an agent with real tools
Define action boundaries, protect secrets, preserve useful logs, and prepare a recoverable deployment before granting an agent access to real systems.
Planning referenceThe server price is only the beginning.
Put compute, model APIs, storage, and setup in one place. Build a 30- and 90-day budget with your own numbers.
Open the cost plannerLess hype. More things you can check.
A useful deployment guide explains who it’s for, what it costs, where the boundaries are, and how to recover when something breaks.
Sources you can follow
Official documentation, dated checks, and a clear line between facts and our recommendations.
Limits you can see
References and worked exercises are labeled separately. Cloud deployment, recovery and performance tests remain unexecuted.
Decisions that stay yours
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