Agent-Ops™ – The DevOps of Agentic AI.

Most companies ship agents like half-finished side projects. Agent-Ops™ standardizes how you design, test, govern, and deploy multi-agent systems — so you get repeatable outcomes, auditable traces, and real production control.

Agent lifecycle Multi-agent orchestration Risk & guardrails Replayable logs
1 · Agent Blueprint Designer
Define agents like real components: roles, tools, memory, guardrails.
Design an Agent
Add as many agents as you need for a scenario.
Agent graph live

This doesn’t just “store form fields.” It builds a structured blueprint you can version, diff, and review.

Current agent blueprint
0 agents defined

Add an agent on the left to see how the blueprint builds up. In the full playbook, this blueprint becomes a version-controlled spec for prompts, tools, and deployment config.

2 · Task Chain Simulator
Turn a business goal into a multi-agent plan with clear handoffs.
Define a goal
We’ll synthesize a chain from your agent blueprints + patterns.

In a full Agent-Ops deployment this ties into a runtime orchestrator and CI tests for each step.

Proposed agent chain
Preview of how your agents collaborate.

Describe a goal on the left, then generate a chain. Each step includes the agent, its action, and the artifact passed forward. Use this in executive workshops to move the conversation from “what is an agent?” to “how do we run 20 of them safely?”

3 · Risk Radar & Replay Logs
See cost, risk, and observability like a production system – not a toy demo.
Risk Radar
Score your scenario before running it for real.
Replay Logs
Step through an agent run like a debugger.
// Click “Generate sample run” to see a realistic trace of agents, tools, and outcomes.

In production, we’d stream structured logs from your orchestrator into this view and tie them back to specific playbook versions and guardrail configs.

4 · Agent-Ops Architecture Map
How this becomes a real platform on Google Cloud, Vertex AI, or your stack.
Core components
Platform view · not vendor-locked.
Ingress
API & Event Gateway
Receives tasks from apps, queues, or cron. Applies auth, rate limits, and routes to the agent orchestrator.
Control
Agent Orchestrator
Manages chains, retries, and fan-out. Talks to Vertex AI, Gemini, or other LLMs through a pluggable adapter.
Safety
Guardrail Service
Pre- and post-checks prompts, responses, and actions for policy, jailbreaks, and sensitive data.
State
Conversation & Memory Store
Vector DB + relational store for session state, so agents can be stateless but not forgetful.
Secrets
Secrets & Config Vault
Central place for API keys, credentials, and per-environment config. Zero trust by default.
Observability
Logs, Metrics, & Traces
Streams all decisions, tool calls, and costs into dashboards for SREs, AI leads, and security.

In the full Agent-Ops™ playbook, this map expands into a deployable reference architecture for Google Cloud, including IAM, network boundaries, and integration with Vertex AI.

Engagement lanes
How MBCC works with you.
  • Agent-Ops Sprint (4–6 weeks): Map 2–3 critical agent use cases, blueprint them, and stand up a minimal observability layer.
  • Platform Build: Extend your existing AI or MLOps stack with Agent-Ops components, pipelines, and dashboards.
  • VCDL-A Partner Enablement: Train internal Vapor Cloud Digital Leaders to run Agent-Ops as a capability, not just a project.

Use this page as the live “show, don’t tell” demo in sales calls, board meetings, or strategy sessions.

Work with MBCC on Agent-Ops™
Use these pages in calls, workshops, or internal pitches.
Sales Page
A narrative page you can send to decision-makers.

The sales page frames Agent-Ops™ as a capability, not a one-off tool. It walks through pains, outcomes, and engagement lanes in executive language.

Open Sales Page
Proposal Generator
Turn discovery into a scoped engagement in minutes.

Capture client context, use cases, and constraints, then generate a structured Agent-Ops™ proposal draft you can refine, price, and send.

Open Proposal Generator