Building Reliable AI Agents: Plugins, Sandboxes, Observability, Stateless MCP, Eval

FriskAI launches with $3.6M to show enterprises what their AI agents are doing. FriskAI ships runtime recording and auditing to expose what agents do in production, giving teams evidence-driven oversight. Outcome engineers can use these traces to build measurement, traceability, and audit trails for validation and incident response (Principles 13,02).

Electric joins Databricks to bring WASM Postgres to AI agent sandboxes. Databricks integrates PGlite WASM Postgres so agents run local Postgres instances in sandboxes that sync back to Lakebase in real time. That makes agent workflows reproducible and testable by preserving transactional context locally while keeping production sync (Principles 07,09,06).

Agent Plugins package your skills, tools, and more. Agent Plugins 1.0.0 standardizes a portable plugin format, enabling interoperable skills and MCP servers across vendors and IDEs. Treating skills as packaged artifacts reduces vendor lock-in and makes skill lifecycle management amenable to CI/CD and review (Principles 06,11).

Scaling AI Agent Infrastructure with the MCP Stateless Updates. Google’s stateless MCP spec replaces stateful agent servers with cloud-native horizontal scaling, MRTR long-running tasks, and SDKs for immediate migration. Designing for stateless agents forces you to externalize state, rethink orchestration, and harden fault recovery for production agent fleets (Principles 06,11,09).

Agent and Model Evaluations in Gemini Enterprise Agent Platform are now GA. Google launches a GA evaluation service with unified metrics, adaptive rubrics, and CI-integrated testing to measure agent quality. Bake these evaluation artifacts into your deployment pipelines to automate validation, gate releases, and maintain continuous outcome audits (Principles 16,14).