[Work-Bench Research] Exploring the Future Compute Stack for AI Agents 🔎
🚀 AI agents aren’t just another workload—they are prompting a reevaluation of the entire infrastructure stack. Here’s what we’re thinking at Work-Bench.
At Work-Bench, we invest in seed-stage enterprise startups and have made early bets on next-gen infrastructure companies such as Goodfire, Runhouse, AutoKitteh, Side-Eye and others.
As part of our research efforts, we actively build out thematic areas where we believe massive shifts are underway—and where new infrastructure companies will emerge.
A shift that we expect to have monumental implications for the entire infrastructure stack is the rise of AI agents—autonomous systems that plan, act, and adapt in real time. As these agents move from proof-of-concept experiments into mission-critical enterprise workflows, they’re pushing the limits of today’s infrastructure stack in ways that mark a shift as foundational as, if not more disruptive than, the cloud-native transformation.
We just published a two-part deep dive on how this shift is unfolding, authored by our very own Priyanka Somrah, and former Work-Bench founder and AI/ML expert Diego Oppenheimer.
🤖 How AI Agents Are Reshaping Infrastructure
AI agents aren’t just running inference—they’re reasoning over long time horizons, managing persistent memory, coordinating with other agents, and making autonomous decisions. That means their compute, memory, and observability requirements look fundamentally different from traditional ML or app workloads.
In our research, we explore two key questions:
📉 Part I: Where Today’s Infrastructure Falls Short
We unpack three core mismatches between AI agents and existing enterprise infrastructure:
Economic inefficiency: Agent workloads are bursty, persistent, and stateful—breaking the assumptions behind today’s resource allocation and autoscaling models.
Technical limitations: There are no first-class abstractions for long-term memory, inter-agent collaboration, or multi-step planning.
Operational gaps: Existing monitoring and debugging tools weren’t designed for dynamic, evolving systems like agents—and it shows.
🏗 Part II: What an Agent-Native Stack Could Look Like
We’re still early—but it’s becoming clear that AI agents will need new infrastructure patterns. In this piece, we explore where things might be headed as teams begin to build systems designed specifically for agentic workloads. A few layers are starting to take shape:
Memory + Context Stores: Going beyond vector databases to support persistent, evolving memory over time.
Coordination + Orchestration: Enabling agents to work together, manage workflows, and handle lifecycle events.
Execution Runtimes: Built for long-running, asynchronous, and loosely connected agent behavior.
These are still early signals, and there’s a lot of experimentation underway—but we’re starting to see the foundations of a new stack take shape. Think of this as a first glimpse into what the future of agent-native infrastructure could look like.
🧬 Why This Matters
We believe AI agents will be foundational to the next era of enterprise software and that deserves thinking through the infrastructure beneath them.
Just as the cloud-native movement gave rise to giants in DevOps, observability, and orchestration, the agent-native era will likely demand a new wave of tooling.
💬 Chat with the Work-Bench Team
If you're a founder building in this space, an enterprise buyer experimenting with agentic workflows, or an SME thinking through how the stack will evolve - let’s talk.
📩 Reach out at priyanka@work-bench.com and diego.oppenheimer@gmail.com