Operate AI agents like mission critical infrastructure.
MajorDomo unifies AgenticOps, AI Gateway, and CloudOps so enterprises can scale GenAI safely, reliably, and cost-effectively.
About MajorDomo
Unified operations for agents, gateways, and GenAI workloads in production.
MajorDomo provides a unified operational platform to run AI agents in production with control and confidence. Our AgenticOps and AI Gateway layers give enterprises a single foundation to deploy, govern, observe, and scale agentic workloads across environments. The platform enforces security, policy, cost controls, and reliability for all model traffic, while ensuring consistent CloudOps discipline for agent releases, behavior, and operations. With MajorDomo, enterprises eliminate operational chaos and run GenAI systems safely, efficiently, and predictably.
AgenticOps Platform
The AgenticOps platform from MajorDomo helps enterprises run AI agents reliably in production—not just build them. It standardizes how agents are deployed, governed, observed, and operated across environments, so teams can scale safely without operational sprawl. AgenticOps gives AI Ops and platform teams control over agent lifecycle, behavior, reliability, and cloud operations as usage grows.
- Agent lifecycle management: Versioning, releases, rollbacks, and controlled rollouts.
- Operational safety & governance: Behavior policies, execution limits, tool-access controls, kill-switches.
- Production observability: Agent-level logs, traces, reasoning visibility, failure modes, and latency signals.
- CloudOps discipline built-in: Standardized configs, consistent environments, autoscaling & capacity control.
- Evaluation-ready operations: CI/CD-native agent evals to prevent regressions before releases.
AI Gateway
A centralized control plane for all AI traffic between your applications and model providers. Standardize access, routing, governance, and observability across models and environments.
- Unified access control: Workspace-level keys, scoped permissions, SSO, RBAC, and secure isolation.
- Routing & guardrails: Provider routing, fallbacks, budgets, quotas, rate limits.
- Deep observability: Logs, metrics, traces, prompt/response capture, latency & token analytics.
- Cost governance: Token accounting and attribution from org, project, user, workspace.
- Model operations: Model catalog, proxy API, transformations, private deployments, low-latency Bi-Frost path.
Ready to run GenAI with control and confidence?
If you are scaling agents across teams and environments, MajorDomo helps you standardize governance, cost, reliability, and operational discipline in one platform.
Blogs
Insights and updates on AI agents, evaluation strategies, and enterprise GenAI operations.
Why Your AI Stack Needs an MCP Gateway — Not Just an API Gateway
As MCP adoption grows, this post explains why traditional API gateways are not enough for production-grade agent systems. It outlines how MCP-aware gateways enable tool-intent routing, semantic caching, persistent session handling, tool-level authorization, and actionable observability that better match stateful, multi-agent MCP workflows.
Why MCP Gateways Need to Own CMID (Especially in a World of Dynamic Agents)
As MCP ecosystems shift toward dynamic, runtime-discovered agent architectures, this post explains why CMID and DCR should move out of individual agents and into the MCP Gateway. Centralizing identity handling reduces operational overhead, prevents identity sprawl, enforces policy consistently, and creates a more stable trust boundary across rapidly changing multi-agent systems.
Agent evaluation: Qualitative aspects
In this final post in the mini-series on Agent evals, the focus shifts to qualitative evaluation using rubric-based metrics across RAG agents, general chatbots, and specialized agents. It covers how LLM-as-a-judge scoring can be aligned with thresholds and practical planning considerations such as retrieval quality, reranking, and metadata labeling for robust real-world evaluation.
Agent Evaluation: Delving deeper
Building on agent architecture foundations, this post dives deeper into testing and evaluation strategies that differ across agent types. We explore LLM structured output testing for specific use-cases like SQL query generation, traditional structured testing for planner-executor models, and generative outcome assessment using LLM-as-a-judge with qualitative rubrics. Learn how to customize evaluation approaches and select the right metrics for real-world agent deployments.
Understanding Agent evaluation
Enterprise business logic has traditionally been deterministic and easily testable, but AI agents introduce new challenges in evaluating reliability and safety. This post explores different architectures for AI integration—from hybrid models with deterministic cores to planner-executor approaches—and the evaluation strategies needed for each to help enterprises confidently migrate to AI-assisted workflows in production.