# Production Agent Systems
All Production Agent Systems notes →1. Latency Optimization
Reducing end-to-end agent response time — model selection tradeoffs (smaller/faster vs. larger/better), reducing tool-call round trips, and where time-to-first-token versus total completion time matters for perceived responsiveness.
2. Parallel Execution
Running independent tool calls and sub-agent tasks concurrently instead of sequentially — fan-out/fan-in patterns, bounding concurrency against provider rate limits, and the correctness hazards of parallel writes to shared agent state.
3. Streaming Optimization
Streaming partial LLM output to the user as tokens generate — chunking strategy for tool-call detection mid-stream, and buffering tradeoffs between responsiveness and being able to cancel or rewrite a bad partial response.
4. Token Optimization
Reducing token consumption without losing task quality — prompt compression, few-shot example pruning, and choosing when to summarize versus truncate conversation history before it's sent to the model.
5. Context Optimization
Deciding what actually belongs in the context window on a given turn — relevance-ranked retrieval over raw dump, context window budget allocation across system prompt, history, and retrieved documents, and the accuracy cost of over-stuffing context versus under-providing it.
6. Semantic Caching
Caching LLM responses by semantic similarity of the input rather than exact match — embedding-based cache key generation, similarity threshold tuning to avoid serving a wrong-but-close cached answer, and cache invalidation when underlying data changes.
7. Response Caching
Exact-match and prefix caching for repeated agent requests — provider-level prompt caching (e.g., cached system prompts) versus application-level response caching, and TTL policy for cache entries that reference time-sensitive data.
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.
9. Capacity Planning
Forecasting compute and provider-quota needs for an agent platform — translating expected request volume into token throughput requirements, provider rate-limit headroom planning, and scaling lead time for a traffic spike that can't be absorbed instantly.
10. Performance Benchmarking
Establishing repeatable load tests for an agent platform — synthetic traffic generation that mimics real tool-call patterns, identifying the actual bottleneck (LLM provider latency vs. tool execution vs. queueing) under load, and regression-testing performance across releases.