Multi-Tenant Architectures
Purpose
[stub: multi-tenant-architectures]
Metadata
| Author | Amit Singh |
| Scope | production-agent-systems |
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Linked from 2 notes
8. Cost Engineering
The engineering levers that actually move agent spend — caching strategy, batching, model routing/tiering, and inference optimization — plus cost attribution by tenant/feature and budget alerting as a first-class signal, feeding the executive ROI numbers in Part 01 of Agentic AI: Projects & Engineering Mastery rather than duplicating them.
Production Agent Systems
A book-shaped table of contents for Production Agent Systems: the runtime substrate, observability, reliability/security/governance, performance/cost engineering, and platform engineering underneath every agent in production. Book 4 of the AI Systems Engineering series.
Related notes
9. Scaling Strategies
Stateless vs. stateful agent design, offloading long-running work to queues and background tasks, horizontal scaling, and rate limiting against the LLM provider.
1. Agent Runtime
The execution substrate that hosts an agent's reasoning loop — process model, container vs. serverless tradeoffs, cold-start latency, and how the runtime enforces max-iteration and timeout limits so a stuck agent doesn't run (and bill) forever.
12. Disaster Recovery
RTO and RPO targets for an agent platform's stateful components — conversation history, vector memory, prompt and model version registry — cross-region failover for the control plane, and the recovery drill that validates a full region loss doesn't silently corrupt in-flight tool calls.
7. Distributed Execution
Running agent workloads across multiple nodes — sharding by session or tenant, coordinating shared state without a single point of failure, and the consistency tradeoffs when two agent instances could act on the same conversation concurrently.