Modiqo raises $3M in pre-seed funding to help AI agents learn by Rote

What's the deal? Modiqo, an agentic AI infrastructure startup, has raised $3M in pre-seed funding to tackle one of the biggest headaches in AI deployment: agents that can't reliably repeat successful tasks. Heavybit and Seligman VenturesDealroom has a profile for this one. Try Dealroom → co-led the round, with participation from Irregular ExpressionsDealroom has a profile for this one. Try Dealroom → and angel investors.

The company's product, called Rote, is a local execution layer that captures what happens during AI agent runs and transforms successful executions into reusable, deterministic workflows. It connects to any API without requiring server infrastructure, sidecars, or SDKs.

Why now? As companies rush to deploy AI agents in production, they're hitting a wall: agents that dazzle in demos but break in the real world. Every time an agent performs a task, it starts from scratch — working from a one-off chat log rather than a proven artifact. Constant changes to underlying models and systems mean the same agent may make different decisions on the same task from one day to the next.

"Today's AI agents are impressive in the demo and unreliable in production," said Heavybit general partner Joseph Ruscio. "Teams pay the same rediscovery tax over and over."

What could go wrong? Modiqo is betting that the reliability gap in agentic AI is a durable problem worth building infrastructure around. But the AI tooling landscape is crowded and fast-moving. Large model providers could bake similar replay and caching capabilities directly into their platforms, squeezing out middleware players. And at $3M in pre-seed funding, Modiqo has limited runway to prove its thesis before needing more capital.

The signal: Co-lead investor Heavybit has built its reputation backing developer-focused infrastructure companies, making its involvement a vote of confidence that the "reliability layer" for AI agents could become a lasting category rather than a feature absorbed by model providers. With enterprises discovering that the real cost of agentic AI lies not in the initial demo but in keeping workflows stable as models and APIs shift, early-stage bets on this plumbing layer suggest investors see the operational pain — not the raw capability — as the next bottleneck worth solving.

Read more: siliconangle.com