AI Coding Agents: Solving the Context Puzzle

Startup fundraising platform VentureCrowd achieved a remarkable 90% reduction in front-end development cycles by deploying AI coding agents. However, this success was preceded by significant trial and error, primarily concerning data and context quality. Agents previously operated with the data accessible at runtime, often leading to confident inaccuracies.

The company also grappled with messy data and unclear processes, as coding agents tended to amplify existing data flaws. This necessitated the creation of a well-structured codebase before effective agent implementation. Chief Product Officer Diego Mogollon emphasized that "challenges are rarely about the coding agents themselves; they are about everything around them," labeling it a "context problem disguised as an AI problem."

This challenge is commonly known as context bloat, where AI systems become overwhelmed by excessive data, tools, or instructions, complicating workflows. While agents require context to function optimally, an overabundance creates noise, increases token usage, slows operations, and escalates costs. Context engineering, which helps agents understand code changes and align with tasks, is one mitigation strategy.

VentureCrowd found a solution in Salesforce's Agentforce Vibes, a platform integrated within the Salesforce ecosystem. Salesforce's update to version 2.0 introduced Abilities and Skills to direct agent behavior more precisely, allowing enterprises to ensure context remains within their data models and codebases. This approach enhances execution rather than solely minimizing context.

Other platforms like Claude Code and OpenAI's Codex manage context differently, focusing on autonomous execution and continuously expanding context. Claude Code employs a context indicator to compact large contexts. Regardless of the method, the overarching trend is that systems manage growing contexts, not limit them, as workflows become more complex, posing ongoing challenges for cost, latency, and reliability in enterprise AI agent deployments.

Builders are advised that more context does not guarantee better results. Investing in context engineering and experimenting with context constraint approaches are crucial. The primary challenge for enterprises lies not in providing more information to agents, but in strategically deciding what information to omit.