Research agents
Agents that collect information, compare sources, summarize findings, and hand off structured output.
AI agent development services
AI agents for workflows that need memory, tools, and action.
MPG ONE builds AI agents that can research, retrieve, classify, draft, check, route, and report inside controlled workflows. The agent is designed around a job, not around hype.
Operational agents
Agents that support tickets, forms, approvals, internal requests, reporting, and follow-up tasks.
Content and SEO agents
Agents that assist with briefs, outlines, internal links, metadata, checks, and publishing workflows.
Developer workflow agents
Agent workflows connected to repositories, checks, MCP servers, and internal engineering routines.
Use cases
Where this service fits.
These pages are built for search intent, but the service itself is built for practical operating needs. If the use case is real, the page should make that clear fast.
- Multi-step research and reporting
- Customer or internal request triage
- AI agents connected to business tools
- MCP-based workflows for developers and teams
- Content operations and SEO QA
- Governed assistant workflows with human review
Operating model
How the work moves from idea to system.
Define the job
We choose a narrow workflow where an agent can create measurable value without taking uncontrolled action.
Design the loop
We define memory, tools, permissions, retrieval, fallbacks, human approval points, and success criteria.
Build the agent
We build the prompts, tool calls, state, logging, checks, and user experience needed for reliable execution.
Harden the system
We test edge cases, reduce failure modes, add visibility, and prepare the workflow for real users.
Deliverables
What you can expect to receive.
- Agent workflow map
- Tool and permission design
- Memory and retrieval strategy
- Prototype agent
- Evaluation set
- Launch and safety checklist
FAQ
Questions people ask before they start.
What makes an AI agent different from a chatbot?
A chatbot mainly responds to messages. An AI agent can be designed to use tools, remember workflow context, take structured steps, and produce an output through a controlled loop.
Can AI agents be used safely inside a company?
Yes, when the workflow has clear permissions, logging, human approvals, tool limits, and evaluation before production use.
Do AI agents need MCP?
Not always. MCP is useful when an agent needs clean access to tools or data sources, but the right architecture depends on the workflow.
AI agent development services