When startup fundraising platform VentureCrowd began deploying AI coding agents, they saw the same gains as other enterprises: they cut the front-end development cycle by 90% in some projects.However, it didn’t come easy or without a lot of trial and error. VentureCrowd’s first challenge revolved around data and context quality, since Diego Mogollon, chief product officer at VentureCrowd, told VentureBeat that “agents reason against whatever data they can access at runtime” and would then be confidently “wrong” because they’re only basing their knowledge on the context given to them.Their other roadblock, like many others, was messy data and unclear processes. Similar to context, Mogollon said coding agents would amplify bad data, so the company had to build a well-structured codebase first. “The challenges are rarely about the coding agents themselves; they are about everything around them,” said Mogollon. “It’s a context problem disguised as an AI problem, and it is the number one failure mode I see across agentic implementations.”Mogollon said VentureCrowd encountered several roadblocks in overhauling its software development. VentureCrowd’s experience illustrates a broader issue in AI agent development. The models are not failing the agents; rather, they become overwhelmed by too much context and too many tools at once. Too much context This comes from a phenomenon called Context bloat, when AI systems accumulate more and more data, tools or instructions, the more complex the workflows become. The problem arises because agents need context to work better, but too much of it creates noise. And the more context an agent has to sift through, the more …
