Complex programmes fail not because of bad code, but because of coordination breakdown. When delivery spans multiple teams, multiple vendors, and multiple workstreams, the friction lives between the agents — human and AI alike. Today's enterprises are deploying AI agents across their SDLC — code generation, testing, security scanning, documentation — but without a shared orchestration layer, these agents create fragmented outputs that programme leaders must still reconcile manually.

NTT DATA's approach treats Atlassian as the agent control plane for programme delivery — the single system of work where Rovo agents, third-party AI tools and human decision-makers coordinate across workstreams in real time. Grounded in the Teamwork Graph, our multi-agent workflows understand team structure, capacity, dependencies, and historical delivery patterns — turning isolated AI capabilities into a governed delivery pipeline. In this session, we'll bring to life multi-agent orchestration triggered at the programme level: from ideation to delivery to release and everything in between!

Key Takeaways:

• Multi-agent orchestration in practice — How to design delivery workflows where multiple specialised agents (Atlassian-native and third-party) delegate, sequence, and validate each other's work across a complex programme, with Jira as the orchestration engine.
• The Teamwork Graph as context backbone — Why grounding agents in organisational context (team structure, historical velocity, dependency maps) delivers materially better outcomes than prompt-engineering alone, and how this applies to cross-team programme coordination.
• Governing AI in delivery without replacing human judgement — A practical framework for where agents decide autonomously, where they escalate, and how programme leaders retain control while eliminating 30%+ of manual coordination overhead.

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