Chronos Agent Builder Studio

From Idea to Production-Grade AI Agents

The execution layer for intelligent agents that don't just answer — they do.

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The Product

A unified stack for intelligent automation.

Build Once, Deploy Anywhere

Chronos Agent Builder Studio is where even novices can go from idea to fully deployed AI agents in a single stack. Instead of stitching together APIs, configs, and dashboards, you describe the agent you want, its role, tools, guardrails, and environment. The studio’s meta-agent (Spark) does the heavy lifting.

Multi-Agent OS

Every agent inherits prebuilt subagents for complex workflows by default.

Marketplace

Clone, tweak, and deploy prebuilt agents from fintech to customer support.

MCP Integration

Host your own MCP servers and connect internal systems securely.

The Meta-Agent

The "architect brain" of the studio. You don't manually wire flows; you describe intent.

  • Translates natural language into agent blueprints
  • Selects and wires tools / MCP servers
  • Applies safety policies & risk gates automatically
Meta Agent Interface
Agent Blueprints from Natural Language
Agent Wiring Diagram
Automated Tool Wiring & Policy Layers

Use Cases

Real-world implementation: Self-Serve AI Agents for Fintech.

Fintech / SaaS Implementation

From Idea → Production without a Giant ML Team

How we expect a mid-sized digital company to use the Chronos AI Agent builder studio to deploy an in-app assistant that executes real actions. For example (refunds, balance checks), and voice agents for support.

The Actors

  • Product Owner (Describes agent)
  • Chronos Meta-Agent (Builds & tests agent)
  • Developers (Create complex workflow logic/MCP tools)

The Goal

  • Deploy In-App Workspace
  • Launch Voice Support Agents
  • Maintain Full Audit Trails

The Value

  • Idea to Production in minutes
  • Unified Stack (Chat + Voice)
  • Compliant Execution Layer
  • Reusable Agent Blueprints
1
Describe the Agent

A Product Owner can prompt Meta-Agent to: "Create an assistant for FAQs, balance checks, and summaries, that can also route complex issues to human agent on intercom." The Meta-Agent will then propose a blueprint/plan before proceeding to create the agent on the studio.

2
Connect MCP Servers

Developer can also create and host a new and custom MCP server for their agent if the tool they desire for their agent isn't on the platform example a "Banking MCP Server" with endpoints like get_balance and initiate_transfer.

3
Define Policy Gates

Users can utilize the policy subagent to create set rules for their Agents to follow: example "Transfers > require human user confirmation." "Refunds > require human admin approval."

4
Design the UX

Define the workspace pattern (charts, tables) and voice scripts for the phone interface.

5
Test in Sandbox

Run against simulated data. Verify the agent properly follows all set rules and properly carry out the desired tasks before deploying.

6
Promotion to Production

One-click deploy. Agents now hit real endpoints but remain guarded by policy gates.

7
Monitor & Iterate

Inspect execution logs, clone agents for new departments, and refine policies.

Agent Chat Interface
Agent Workspace: Chat & Structured Data
Policy Configuration
Policy & Permission Configuration

Enterprise Capabilities

Strategic partnerships for robust automation.

Omnichannel Support

Agents that live across web, mobile, sandboxed environments and in-app experiences. They route issues, pull data, and maintain audit trails.

Intelligent Voice Agents

Inbound and outbound calls capable of handling complex workflows naturally, from lead qual to troubleshooting.

Contact Us on WhatsApp for partnership to deploy your own working AI ASSISTANT Agent

About Chronos

Chronos Intelligence Systems is an AI infrastructure company focused on building the execution layer for intelligent agentic systems that don’t just generate answers, but reliably do things in the real world.

Founded on 24th September 2025, we work with companies/enterprises that need more than chatbots to automate real-world tasks with less human oversight. By combining robust orchestration, compliance-ready logging, we're also introducing a meta-agent named (Spark) that'll help users build AI agents via natural language, to make production-grade AI accessible to almost anyone.

Jesse Newton Okoroma

Jesse Newton Okoroma

Founder & CEO

Jesse is a LLM engineer and AI architect focused on one question: how do we get agents to reliably execute in the messy real world? He founded Chronos to build the infrastructure for large-scale, policy-aware multi-agent systems. His background spans LLM fine-tuning, agentic system design, and deploying AI into high-stakes domains like trading, finance and EdTech.

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