This topic examines architectures and safety protocols for “self‑sovereign” AI stacks — systems that prioritize user control, transparency, and local authority over agent behavior — in the context of Vitalik Buterin’s proposal and comparable frameworks. It focuses on practical building blocks: agent observability (audit logs, telemetry, attestations), tool and chat API integrations, cloud platform connectors, on‑device LLM inference, and credential management. Relevance in 2026: as LLM agents are embedded across personal devices and cloud services, demand has risen for standardized ways to constrain, inspect, and revoke agent capabilities. Trends include adoption of the Model Context Protocol (MCP) to mediate external tool access, sandboxed execution for untrusted code, and richer observability for user oversight and regulatory compliance. Key tools and roles: Semgrep provides fast, open‑source static analysis to enforce secure coding guardrails and detect policy violations; pydantic’s mcp-run-python offers a sandboxed MCP tool runtime (via Deno/Pyodide) so agents can run controlled Python; Cloudflare hosts MCP server integrations (Workers/KV/R2/D1) for scalable in‑cloud MCP endpoints; Kiln exposes MCP connectivity to orchestrate tasks and external tools; MCP Toolbox for Databases simplifies secure database connectors with pooling and credential handling. Together these components enable confined agent capabilities, authenticated tool access, and audit trails. Practical implications: implementers should pair MCP‑mediated tool access with sandboxed execution, credential scopes and rotation, and fine‑grained observability (A2A logs, attestations, user revocation APIs). This combination supports user control without sacrificing interoperability across cloud, chat APIs, and on‑device inference contexts.