Deep dives on production AI agents, compliance engineering, token optimization, and the architecture decisions behind MeshFlow.
Parallel crews multiply repeated context, model selection mistakes, and hidden retry costs. This guide walks through the MeshFlow controls designed to make those costs visible and governable.
HIPAA's technical safeguards are specific. Minimum-necessary filtering, audit controls, access controls. Here's how MeshFlow implements each one — and what you'd need to write manually in LangGraph.
Self-hosted agent builders need patch discipline, network isolation, secret handling, and auditability. Here is the production checklist we use when evaluating workflow infrastructure.
Step-by-step: a 3-agent crew that processes patient intake forms, redacts PII automatically, generates a SHA-256 audit trail, and applies token controls from the first run.
MeshFlow Cloud extends the framework with shared dashboards, ModelRouter insights, cost regression gates, and managed compliance evidence for teams.
Durable execution is hard. Distributed state, partial failures, re-execution semantics. Here's the architecture behind DurableWorkflowExecutor and why we chose write-ahead logging over event sourcing.
Both frameworks claim production-readiness. One ships with compliance, cost governance, and audit trails. The other ships with better time-travel debugging. We ran both on the same task. Here's what we found.