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OpenDerisk

by derisk-ai

AI-native risk intelligence and SRE for multi-agent deployments

Python
Updated Jul 13, 2026
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Summary

Provides an AI-native risk intelligence manager that monitors and mitigates application and agent-driven risks around the clock. Combines multi-agent orchestration and SRE-style telemetry to detect anomalies, manage failure modes, and enforce risk policies across agent interactions. Distinctive features include continuous risk scoring and automated safeguards tailored for multi-agent deployments, leveraging the Orchestrator-Worker Pattern and the Hierarchical Multi-Agent Pattern.

Key Benefits

As agents operate autonomously and delegate tasks, operational visibility into failures and risk becomes essential for trust. OpenDeRisk brings SRE practices into the agent era, letting teams detect systemic failure modes, quantify agent reliability, and enforce runtime protections. This matters because reputation and operational reliability are what turn experimental agent stacks into production-grade services. Enhancing safety with Human-in-the-Loop can further improve decision fidelity.

Ideal For

SRE and platform teams running production multi-agent systems that need continuous risk monitoring and automated mitigation. For teams adopting standard interaction and protocol practices, consider integrating with the Model Context Protocol (MCP) to standardize agent communications.

Real-World Examples

  • Detecting and alerting on multi-agent system failures and anomalous agent behavior
  • Applying continuous risk scoring to agent interactions before promotion to production
  • Automating mitigation (circuit breakers, throttles, rollbacks) for unreliable agents
Topics
agentai-sreaigcdevopsmcpmulti-agent-systemsmulti-agents-orchestrationragriskrl+1 more
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Keywords
multi-agent reliabilityagent failure modesproduction agent monitoringmulti-agent trust