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Vantage
Illustrated scenario: scaling agent workflows with nala
Vantage, a fast-growing fintech platform, needed to coordinate multiple AI coding agents across a complex monorepo. With nala, they cut feature delivery time by 40% and brought every agent under a single orchestration layer.
Illustrated scenario · fictional team, real workflow
Table of contents
CHALLENGE
Vantage's engineering team had adopted multiple AI coding agents (Claude Code, Codex, and Cursor) but each operated in isolation. Context was lost between sessions, agents duplicated work, and code reviews became a bottleneck as engineers struggled to reconcile conflicting outputs. The team needed a way to orchestrate all their agents from a single workspace, with shared memory and clear handoffs.
“Before nala, our agents were stepping on each other constantly. One would rewrite a module while another was refactoring the same file. nala gave them a shared workspace where they could actually collaborate instead of compete.”
SOLUTION
Vantage deployed nala across their 12-person engineering team, connecting it to their existing Claude Code and Codex subscriptions. The team created shared Epics for each feature, breaking work into scoped tickets that agents could pick up independently.
Using nala's agent-to-agent communication, the team set up automated review handoffs: when one agent finished a ticket, it would notify a review agent that checked the diff against the spec before passing it to a human reviewer. This cut review cycles from 2 days to under 4 hours.
The team also leveraged nala's memory system to persist architectural decisions and coding conventions across sessions, so every agent started with the same context. No more re-explaining the codebase on each run.
IMPACT
“nala didn't just make our agents faster. It made them work together. We shipped a major payments integration in 3 weeks instead of the 5 we'd budgeted. The orchestration layer paid for itself in the first sprint.”
Within the first quarter, Vantage reduced average feature delivery time by 40%, eliminated agent context-switching overhead, and brought their code review backlog to zero. The team now runs 8-10 concurrent agent sessions with full visibility into what each one is doing.
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