Agent infrastructure: latency, MCPs, memory, and retrieval

Agentic AI has a latency problem that more compute won’t solve. The piece shows end-to-end agentic workflows suffer multi-hop, CPU-bound latency that GPUs alone don’t fix. Outcome engineers must measure and architect for holistic latency—coordination, CPU work, and orchestration matter as much as model throughput (Principle 09 & 16).

Open Sourcing Comfy MCP on Local. Comfy releases an open-source MCP that runs locally and lets agents manage hardware-aware ComfyUI workflows across cloud and edge. That gives teams a reproducible, air-gapped control plane for local agent development and device-aware artifact production (Principle 06 & 07).

Agent, skill, or MCP? Which to use and when to use them | AWS’ Clare Liguori. Clare Liguori lays out an MCP-first pattern and when to choose skills, full agents, or stateless MCP servers to scale enterprise AI. Use this as an architecture decision guide—pick the granularity that keeps your graph and context layer manageable while enabling composability (Principle 06 & 11).

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers. Sentence Transformers v6.0 adds a MultiVectorEncoder for ColBERT-style late-interaction retrieval that improves token-level matching and visual document retrieval. Practitioners building RAG and agent tool-chains can use late-interaction encoders to raise retrieval precision and lower hallucination risk in multi-hop agent tasks (Principle 06 & 12).

How Much Memory Does Your Agent Actually Need?. IBM Research shows that calibrated, selective retrieval and self-distillation often outperform dumping full guidelines into context, saving tokens while improving task completion. Outcome engineers should treat memory dosage as a tunable system parameter—optimize retrieval policies and memory size for capability and cost, not just brute-force context injection (Principle 06 & 12).