Agentic Stack: latency, retrieval, MCPs, memory, and doc extraction

Agentic AI has a latency problem that more compute won’t solve. The piece documents end-to-end latency caused by multi-hop, CPU-bound work in agentic systems and shows GPUs alone don’t solve production bottlenecks. Outcome engineers must redesign orchestration and work partitioning (async workers, spot CPU pipelines) to meet SLAs — Principle 09/16.

Databricks Document Intelligence: pushing the frontier for complex document extraction. Databricks ships Precision Mode, an agentic, parallel extraction system that improves accuracy on complex enterprise documents. If you build document-first agents, this changes how you design extraction pipelines, tool orchestration, and verification — Principle 06/09.

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers. Hugging Face adds a MultiVectorEncoder for ColBERT-style late-interaction retrieval, enabling token-level matching and visual-document retrieval. That capability shifts index design and retrieval-cost tradeoffs: plan for multi-vector storage, late interaction compute, and tighter relevance signals in your retrieval stack — Principle 06/12.

Open Sourcing Comfy MCP on Local. Comfy opens its MCP for local, hardware-aware agent orchestration so teams can run ComfyUI workflows across local and cloud. Running MCP locally lets outcome engineers iterate faster, reduce cloud dependency, and enforce hardware-aware scheduling for production agents — Principle 07/06.

How Much Memory Does Your Agent Actually Need?. IBM Research shows selective retrieval and calibrated memory dosage often outperform dumping full guidelines into context, saving tokens while improving task completion. Use this evidence to design retrieval policies and memory budgets that balance cost, fidelity, and verifiability in agent behavior — Principle 06/12.