The MeshFlow Blog
Deep dives on production AI agents, compliance engineering, token optimization, and the architecture decisions behind MeshFlow.
How Token Costs Grow at Multi-Agent Scale — And Where to Control Them
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.
agent teams
HIPAA and AI Agents: What "Technical Safeguards" Actually Require From Your Code
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.
Security Checklist for Self-Hosted Visual Workflow Builders
Self-hosted agent builders need patch discipline, network isolation, secret handling, and auditability. Here is the production checklist we use when evaluating workflow infrastructure.
Build a HIPAA-Compliant Patient Intake Agent in 45 Minutes
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.
Designing MeshFlow Cloud: Token Analytics, CI Cost Gates, and Audit Reports
MeshFlow Cloud extends the framework with shared dashboards, ModelRouter insights, cost regression gates, and managed compliance evidence for teams.
DurableWorkflowExecutor: How We Built Crash-Proof Agents Without a Distributed Transactions Framework
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.
LangGraph vs MeshFlow: A Side-By-Side Production Evaluation
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.