AI Gateways
Purpose
[stub: ai-gateways]
Metadata
| Author | Amit Singh |
| Scope | production-agent-systems |
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Linked from 3 notes
10. The AI Ecosystem
Maps the current AI ecosystem — model providers, orchestration frameworks, vector databases, evaluation tooling, and inference infrastructure — as the landscape an agentic system architect has to navigate.
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
3. Agent APIs
Defines the contract layer for exposing agents as internal APIs - request/response schemas, streaming vs synchronous invocation, idempotency keys, and versioning strategy for breaking prompt or tool changes.
5. Agent Registries
Covers building a central registry of agents and tools with ownership metadata, capability tags, and discovery APIs, mirroring a service catalog but for autonomous and semi-autonomous agents.
2. Agent SDKs
Compares the design trade-offs of building a first-party agent SDK (LangGraph, custom Python/TypeScript wrappers) against adopting a vendor SDK, focused on API stability, versioning, and abstraction leakage at scale.
8. Deployment Strategies
Applies canary, blue-green, and shadow-deployment patterns specifically to agent releases, where a bad deploy can mean bad tool calls or unsafe actions rather than just bad HTTP responses.