gjalla

Engineering teams using AI coding agents are accountable for code they didn't write and increasingly can't tell what those agents read or why they produced a given output. Standards and rules get repeated to agents over and over without sticking, the same mistakes recur, and docs or decision records drift out of sync with the code while agents keep reading them anyway. This shows up as more production incidents, rework, and brittleness even as shipping speed increases, with token costs rising as agents repeatedly relearn things they already worked out. Teams lack a common baseline that consistently applies their processes and standards across every agent, repo, and teammate.

gjalla acts as a home for the layer of data and process that coding agents actually run on, replacing tools built for human workflows with one designed for agentic ones. It lets teams observe what each agent read, what it changed, and whether that work became a feature, bug fix, or rework, connecting agent activity to shipped outcomes. It governs by maintaining one versioned source of truth across agents, repositories, and teammates so agents only rely on vetted data. It also supports learning, so agents don't keep paying to relearn the same lessons, letting the system compound safely as the business evolves. Mentioned integrations include GitHub PR impact assessments and weekly digests summarizing what changed.

Coding agents unlocked speed but visibility is disappearing, rework is growing, and costs are becoming harder to justify, creating a gap between how fast teams ship and how much control they have over agent behavior.