A multi-agent solution with 50+ system integrations
Brillio ran the transformation internally, using our own Digital Workplace Services and Digital Office operations as the proving ground for our Agentic AI capabilities. ADAM was the natural foundation – it already provided a marketplace of reusable, purpose-built agents, a tooling-agnostic integration layer with 50+ pre-built connectors for the systems we live in (GitHub, ServiceNow, Splunk, and more), and design principles we already trusted. So rather than bolt AI onto the old workflow, Brillio rebuilt intake, triage, diagnosis, and resolution around it end to end.
Under the hood, the pieces feed each other in a continuous loop. Instrumentation and telemetry – metrics, traces, logs, events, and threat signals – flow into an n8n orchestration engine that reasons, plans, acts, observes, and reacts. That engine draws on a RAG pipeline over a vector database for historical context, reaches into ServiceNow and our monitoring tools through an MCP server layer, and surfaces everything through persona-based Power BI dashboards for CXOs, SRE, and product teams. Sitting above it all, an ADAM Control Tower handles governance and keeps the agents continuously evaluated and fine-tuned.
The model is carried by a set of single-purpose agents, each doing one job well before handing off to the next:
Orchestrator Agent – classifies every alert as actionable or noise
Metrics & Logs Diagnosis Agents – run root-cause analysis against historical resolutions and SOPs
Deduplication Agent – stops redundant tickets before they land
Ticket Triaging Agent – auto-creates, categorizes, and assigns by skill, shift, and workload
Observability Agent – continuously evaluates health across infrastructure, applications, and networks
On top of that core loop, Brillio automated the high-volume, low-thought work that quietly eats an IT team’s day, from a Welcome Email Agent that handles new-joiner onboarding to automated resolution of AD account lockouts. What holds the whole design together is a set of deliberate choices: atomic agents that compose into full workflows, contextual short- and long-term memory so context carries across steps, built-in explainability and auditability, and human-in-the-loop checkpoints wherever a decision carries real risk.
A five-stage model moved from discovery and analysis, through roadmap definition, build and integration, and validation at scale, to a final stage of operating-model and change management that ran in parallel throughout. In practice that landed as two sprints: a Phase 1 “Quick Wins” – alert rationalization, smart triaging for three core Digital Office apps, and automation of routine tasks like access requests and OS patching – followed by a Phase 2 “Scaling & Expansion” sprint that replicated the blueprint across more SaaS and in-house applications and added infrastructure monitoring, asset-lifecycle, and RCA automation. We co-designed with our IT Ops team from day one and worked alongside our security team on guardrails the whole way through.
