Agentic Orchestration for Engineering Intelligence

Overview

Aurvia Group built Operion, an engineering intelligence platform, by applying the same leadership principles used to scale human teams to a network of AI agents. Using CrewAI and the Model Context Protocol (MCP), we organized 11 specialized AI agents into 9 distinct “crews” — each acting like a delivery team with clear roles, accountability, and measurable outcomes. This approach created a fast, reliable way to unify fragmented engineering data and surface actionable insights for technology and business leaders.

Challenge

Engineering organizations often operate with:

• Fragmented visibility: Delivery metrics scattered across Jira, Confluence, CI/CD, and code repositories.
• Manual, inconsistent reporting: Teams spend hours preparing updates for leadership with no standardized KPI definitions.
• Slow risk detection: Delivery slippage and quality regressions aren’t visible until late in the cycle.
• Unstructured AI experimentation: Without orchestration, agent efforts become chaotic, costly “AI slop.”

We wanted to prove that agentic AI — if led with intent and governance — could replace manual reporting and unify engineering intelligence at scale.

Solution

We created an agentic orchestration model inspired by how effective leaders run teams:

• Specialized AI Crews:

• Development Crew (frontend, backend, QA, testing)

• Analytics Crew (dashboards, AI insights)

• Security Crew (security audits)

• Enterprise Crew (auth, payments, marketing resources, config)

• Auth Crew (auth setup, config agent)

• Payment Crew (integration and backend)

• Marketing Crew (content and messaging)

• Seed Data Crew (data bootstrapping)

• Full Platform Crew (manager LLM orchestrating all crews)

• MCP Shared Toolset: Standard tools for repo access, build and deploy pipelines, testing, quality gates, and observability so every crew could work against the same infrastructure safely and consistently.

• Conversational + Visual Intelligence: Agents ingest Jira issues, Confluence docs, and pipeline data; standardize KPIs like velocity, Dev-to-Done cycle time, defect escape rate; and generate dashboards and natural-language insights for leaders.

• Governed Process: A manager LLM acts like an engineering program lead — enforcing standards, resolving dependencies between crews, and ensuring outputs converge into a coherent product.

This architecture became the backbone of Operion, Aurvia’s AI-powered engineering productivity and reporting SaaS.

Results

• Development Speed: Rapidly stood up 11 agents across 9 specialized crews to deliver a functional prototype in weeks
• Reporting Time: Early pilots showed ~60–70% reduction in manual reporting effort compared to baseline
• Visibility: Achieved standardized KPI definitions and unified dashboards across multiple engineering projects
• Executive Feedback: Initial leaders and program managers adopted and validated the dashboard concept as significantly clearer than prior status reports
• Engineering Hours Saved (Projected): Expected to save hundreds of hours per quarter as automation replaces manual Jira/Confluence roll-ups
• Data Confidence: Built direct integration with source systems to ensure accuracy and reduce human error

The result was a production-ready platform proving that disciplined AI orchestration can deliver reliable, actionable engineering intelligence and free teams to focus on execution rather than reporting.

---

This case study highlights Aurvia Group’s Operion App product — an AI-powered analytics and productivity platform built to help technology leaders gain clarity, speed, and confidence in delivery decisions.