MCP Dev Summit Mumbai 2026

The dominant observability pattern for AI agents today is layered agents: sidecars, daemon sets, exporters. Each adds blast radius.

This talk proposes flipping the model, using eBPF for zero-agent kernel telemetry, exposing it via MCP, and letting the LLM itself do the orchestration across tool calls.

I'll demo a concrete multi-tool MCP interaction: an LLM receives "why is this service slow?", autonomously calls get_recent_process_execs, correlates with get_active_connections, and follows up with get_high_syscall_latency, forming a causal chain from kernel truth to natural language diagnosis. No pre-scripted runbook. No human in the loop.

The talk goes deep on failure modes specific to this architecture: tool-call loops triggered by ambiguous latency signals, hallucination risk when eBPF data is sparse, and retry/fallback strategies when kernel probes detach under load.

We'll also cover how to design MCP tool responses that constrain LLM reasoning toward actionable conclusions, not just open-ended exploration.

Attendees leave understanding how to architect kernel-aware agentic systems that are genuinely production-safe.