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Description
An agent that cannot reach the systems a business already runs is a chat window. Most teams stall exactly here, because giving a model real access safely is a different problem from prompting it well. This covers the connection layer end to end, using Model Context Protocol. Typed tools instead of scraped screens, read access kept separate from anything that can change or delete data, credentials checked before a first write, and the failure modes that tend to show up only under real load. Built on open source servers I publish for infrastructure, ERP, marketplace and booking platforms. The code is on GitHub and readable before you decide.
What you'll get from this package
One connector exposing your own system to an agent, running locally on your machine
A permission model that keeps read access separate from anything destructive
Additional details
Coaching delivered via live sessions.
Three to four sessions. We pick one system your agent needs to reach, design the tool surface together, build the connector live, then test it against the cases that break things in practice, a bad credential, a timeout, a malformed response.
Talk to a team member
Schedule a call with a Leland team member who can help you explore your options.
Schedule a call
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Mirza also coaches for Break Into AI Careers, AI for Data & Analytics, AI for Finance, AI for Product Development, AI for Sales & Marketing, Build with AI, and AI Services. View all.
Offered by Mirza I.
Joined February 2026
4.93
Production AI Is a Different Problem (AI & ML Engineering)
Official Mastra AI Agents Ambassador, building production RAG pipelines, MCP server connectivity, and multi-agent orchestration systems for enterprise clients. Udacity Machine Learning Engineer nanodegree. Sixteen years of cloud architecture across GCP, AWS, and Azure. Production AI systems delivered for Accenture and Capco clients in highly regulated industries where failure has real consequences. The sessions teach the engineering decisions that keep AI systems running past month one. Ideal for : Software engineers, ML practitioners, and technical leads building AI systems that need to survive contact with real data
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