Build production-ready AI agents with persistent memory, audit trails, and human-in-the-loop approvals. Combines multi-agent memory management, governance controls, and monitoring for enterprise deployments.
| # | Tool | Role | Website |
|---|---|---|---|
| 1 | Nova | Nova - Self-hosted agentic workflows with approval gates | https://mynova.space |
| 2 | Loop me in | Loop me in - Human judgment escalation for edge cases | https://loopmein.ai |
Step 1: Set Up Shared Agent Memory Deploy CMEM as your central memory layer. Configure vector search over agent decision logs and conversation history. Connect your Claude Code or coding agent framework via CLI/IDE integrations. This ensures all agents share context and avoid duplicated reasoning.
Step 2: Create Audit & Access Controls Use Setoku to build live-data dashboards that expose agent metrics (decisions made, cost, latency). Configure read-only access for stakeholders and audit logs for compliance. Setoku's MCP compatibility means your agents can query their own performance data.
Step 3: Deploy Self-Hosted Workflows Set up Nova for production agentic workflows. Configure approval requirements for high-stakes actions (code deployments, data modifications). Route decisions through Axtary's local-first policy engine to enforce governance rules before execution.
Step 4: Add Evaluation & Rollback Integrate Kalytera to run continuous evaluation on agent outputs. Set rollback thresholds for quality degradation. Pair with Loop me in to escalate ambiguous decisions to domain experts for real-time feedback.
Step 5: Monitor & Iterate Use CMEM's vector search to analyze patterns in agent decisions. Feed insights back into prompt refinement cycles. Track metrics in Setoku dashboards to measure improvement over time.
