The hard part of enterprise AI is no longer adopting it — it's operating it at scale. MajorDomo AgenticOps is the operational control plane that spans the full lifecycle: build, deploy & orchestrate, govern, and operate. These are the jobs teams bring to it, grouped by what you're trying to achieve.
One unified platform across every stage of the AI delivery lifecycle. Start with your most urgent problem and grow into the rest — every deployment captures operational intelligence that makes the next one faster, smarter, and more autonomous.
AI-driven build: generate code, MCP servers, and integrations with governance built in — so projects move from pilot to production instead of stalling in between.
Enterprises want AI to write code but can't trust or govern how it reaches production — re-reviewing everything erases the gain.
A governed issue-to-PR pipeline with human checkpoints, deterministic confidence scoring, and a PR qualification gate before merge.
Building MCP servers by hand is slow, specialized work — integration backlogs run weeks per system.
Upload an OpenAPI spec or design conversationally; MCPOps generates source, Dockerfile, Helm, and K8s artifacts straight into GitHub.
Mapping every REST endpoint to a tool produces servers with hundreds of confusing tools that agents mis-select.
MCPOps aggregates related APIs into business capabilities — one “Review Customer Account” tool instead of a dozen raw calls.
Developers adopt Codex, Claude Code, and Copilot individually — no central visibility, cost control, or policy.
Point any coding tool at the gateway endpoint to gain centralized routing, budgets, and observability, with rollout by team or model.
Ship agents and models into production, roll back in seconds when something regresses, and run them wherever your data has to live.
Agents change weekly, yet each change is an untracked production gamble with no way back when behaviour regresses.
Versioned agent artifacts, canary deployments with auto-promote, kill-switches, and instant rollback to any prior version.
Cloud and Kubernetes environments are slow to provision, and cost or compliance issues surface only after they're live.
Low-code, guardrailed self-service provisioning with security, compliance, and cost checks left-shifted into design time.
Enterprises need AI close to their data, but operating gateways and models per environment causes fragmentation and drift.
One global Director manages policy and configuration; independent Worker data planes execute locally per project, team, or region.
Regulated data can't leave its environment or jurisdiction, excluding whole industries from centralized AI services.
Inference runs inside your own VPC, on-prem, or air-gapped environment with regional routing — data never leaves the perimeter.
Put one governed path in front of every model, agent, and MCP call — so security, guardrails, policy, and spend are enforced, not assumed.
API keys sprawl across teams and apps can reach models directly, so every control becomes optional and nothing is auditable.
A federated Director defines policy centrally while in-line Worker nodes enforce identity, guardrails, budgets, and telemetry on every request — with nothing to bypass.
Every prompt is an attack surface — prompt injection, jailbreaks, and sensitive data leaving in requests or responses, with no consistent control across teams.
Real-time guardrails run in-line at the gateway: prompt-injection and jailbreak detection, PII/secret redaction, and content policy — applied to every request and response.
Provider invoices show a total, not who spent it — and budgets can't be enforced before the money is gone.
Token-level attribution from org to project, user, and agent; hard budgets enforced in-line before a call reaches a model; cost- and quality-aware routing.
Most enterprises have no record of what their AI actually did — a growing liability under the EU AI Act.
Every interaction produces a full trace — identity, prompt, routing, tool calls, tokens, cost — captured into immutable audit logs with end-to-end traceability.
Keep production AI reliable and affordable: control cost, catch drift, prove quality on every change, and turn each deployment into operational intelligence that compounds.
Long coding sessions resend stale files, logs, and tool output on every call, inflating prompts far beyond the task.
The Context Optimizer compacts history, trims noisy payloads, and tiers simple requests to lower-cost models — inline, with no app changes.
Agents fail silently — drifting and regressing on prompt, model, or data changes, caught only after customers complain.
CI/CD-native offline evals run on every change and block anything that regresses on quality, safety, or cost before it reaches production.
Golden datasets pass while real users fail, and no one can tell whether the cause is a prompt, a deploy, or a model update.
Continuous online scoring of live traffic correlated with offline results pinpoints where quality diverges and why.
Dozens of teams ship agents on different stacks — the platform team owns a fleet it can't see or standardize.
Standardized configs, one policy model, and a central inventory of agents, versions, tools, and owners across the org.
Land on your most pressing pain — governing spend, gating releases, standing up MCP servers — then expand across the full delivery lifecycle on one operational control plane.
Tell us where your operational load is heaviest. We'll show you what Operational Intelligence captures there, and what it lets you stop doing by hand.
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