Lesson 2096 of 2116
AI Agent Deployment Modes: Sync, Async, Streaming, and Batch
Pick the right deployment topology for your AI agent's latency and durability needs.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2streaming
- 3async jobs
- 4batch inference
Concept cluster
Terms to connect while reading
Section 1
The premise
AI agents deploy as synchronous request-response, async background jobs, streaming token responses, or batch inference — each fits distinct latency, durability, and cost profiles.
What AI does well here
- Producing streaming token output when the runtime supports it
- Returning partial results progressively for long tasks
- Resuming from queue checkpoints in async deployments
- Batching similar requests when prompted to do so
What AI cannot do
- Decide on its own which deployment mode best fits a use case
- Maintain conversational context across job-queue boundaries without explicit state
Key terms in this lesson
End-of-lesson quiz
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