MCP Dev Summit Bengaluru 2026

What happens when your on-call agent can read Datadog metrics, tail logs in groundcover, pull pod state from Kubernetes, and grep GitLab for the commit that broke prod, all in one conversation? At Learneo we built one MCP endpoint that does that across 11 upstream servers, 518 indexed tools, and can manage multiple AWS accounts credentials. It changed how we triage incidents.
I'll walk a synthetic incident end to end, with redacted screenshots from real ones, showing the agent go from alert to MR to revert.

Key Takeaways:

- Context: meta-tools and BM25 search keep the agent at about 1.5k tokens, not 500 schemas, and it pulls what it needs on demand
- Routing: a company overview shipped as MCP instructions on connect points the agent at the right cluster and Jira project before it picks a tool
- Memory: an agent-maintained company wiki for org structure, data models, and playbooks. The agent reads it and writes back what it learns
- Security: centralized credentials and a write denylist so the agent can read everything without breaking anything

Attendees will leave understanding both the value and the architecture for turning MCP into production-grade agent infrastructure.