AI agent infrastructure: from always-on clusters to agent-aware platforms
AI agent infrastructure refers to the cloud, data and orchestration layers purpose-built to run autonomous or semi-autonomous software agents that interact with multiple systems, exhibit bursty usage patterns, and require fine-grained monitoring, evaluation and cost control across the full lifecycle from training to production. That definition is starting to reshape enterprise architecture redesign efforts across major platforms. Traditional cloud designs assumed steady, long-lived services; AI agents behave differently, triggering heavy compute in short spikes and then sitting idle for long periods. This mismatch drives over-provisioning and weak observability for multi-step, multi-system automation. In response, cloud and SaaS vendors are building serverless AI workloads that scale to zero, specialized observability for agent flows, and tighter feedback loops between production and development. The aim is to support dense, cost-efficient multi-system automation while keeping agents reliable under real-world traffic.
AWS OpenSearch Serverless: rebuilt around bursty agent workloads
AWS has rebuilt almost all of Amazon OpenSearch Serverless to fit AI agent workloads, which arrive in bursts and then go quiet. OpenSearch now separates storage and compute on a new proprietary storage layer and supports collections that “can truly shrink all the way to zero,” so customers do not pay when resources are idle. According to AWS OpenSearch general manager Tia White, “about 97 percent of it has been built from the ground up by the engineers on the managed service.” The new design aims to cut costs by up to 60 percent compared with provisioned clusters running at peak capacity, helped by compression in the storage layer and an autoscaler that scales down within seconds. It also auto-scales around 20 times faster than the previous generation and supports both search and vector collection types, aligning the service with agent memory, semantic search and log analytics scenarios.
Asana and StackAI: connecting agents to every enterprise system
While AWS is rebuilding its data layer, SaaS platforms are rethinking how agents act across workflows. Asana’s acquisition of StackAI signals that AI agent infrastructure is no longer just about language models inside a single product, but about multi-system automation across CRMs, ticketing tools, data warehouses and custom apps. StackAI’s technology focuses on letting agents call many back-end systems, orchestrate tasks, and push results back into work management flows. That direction reflects a broader enterprise architecture redesign: instead of integrating AI features one at a time, platforms want a shared agentic layer that can authenticate to services, route actions, and enforce permissions consistently. With this approach, AI agents stop being isolated copilots and become first-class actors that can execute full workflows, hand off tasks between systems, and maintain context as they move across the enterprise application landscape.
Closing the loop: agent observability, evaluation and continuous improvement
Agent-centric platforms also need a closed feedback loop between production behavior and model improvement. CoreWeave’s architecture, built with Weights & Biases tools, shows how serverless AI workloads for reinforcement learning, inference and observability can be tied together. Serverless RL handles post-training, scaling elastically with training workloads and cutting costs by up to 40 percent, while separate always-on instances run training and inference to shrink iteration cycles from hours to seconds. CoreWeave Inference adds continuous monitoring of performance, scaling and health under real-world traffic, and W&B Weave provides observability tuned for multi-agent workflows rather than one-off model predictions. W&B Skills and an MCP server then turn coding agents into AI researchers that work continuously on reliability gaps. According to Futurum’s Nick Patience, closing this production-to-development loop gives enterprises a meaningful advantage when making agentic AI ready for users.
Cost optimization and system integration reshape AI agent infrastructure
Across these moves, two forces are driving enterprise architecture redesign: cost optimization and deep system integration. AWS’s OpenSearch Serverless overhaul targets cost optimization agents indirectly by matching capacity to bursty loads and scaling to zero, instead of running peak-sized clusters all the time. Asana’s StackAI deal aims at integration, letting AI agents coordinate work across many systems rather than living inside a single UI. CoreWeave and Weights & Biases focus on compressing the build–measure–learn cycle, so teams can improve agents based on production signals without months of offline testing. Together, these trends point toward a new AI agent infrastructure stack: elastic serverless data planes, agent-aware observability and evaluation, and orchestration layers that plug into every major enterprise system. The platforms that align these pieces will be better placed to deliver reliable, cost-efficient multi-system automation at scale.