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Ferwa Zaidi

As seen in: BMC Software
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Articles

Orchestrating Trustworthy AI Agents: A Production Governance Pipeline with Control-M, Amazon Bedrock, and Snowflake

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By Michael Oladugba, Sam Duru-Nnebue, Ferwa Zaidi
| BMC Software
Learn how Control-M orchestrates a production-grade AI governance pipeline with Amazon Bedrock, Snowflake, DataBrew, SES, and QuickSight — reliably. Getting an AI agent to produce a compelling response in a playground is straightforward. Getting that same agent to produce reliable, trustworthy output every quarter – on fresh data, in the right sequence, with results delivered to the right people and systems – is an entirely different problem.
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Operationalizing AI-Governed Market Intelligence with Control-M, Snowflake, Amazon Bedrock, AWS DataBrew, Amazon SES, and Amazon QuickSight

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By Michael Oladugba, Sam Duru-Nnebue, Ferwa Zaidi
| s7280.pcdn.co
The challenge with putting AI agents into production Getting an AI agent to produce a compelling response in a playground is straightforward. Getting that same agent to produce reliable, trustworthy output every quarter – on fresh data, in the right sequence, with results delivered to the right people and systems – is an entirely different problem. The gap between a demo and a production AI workflow usually is not the agent itself.
Open in Who Shared Issue with article?

Operationalizing AI-Governed Market Intelligence with Control-M, Snowflake, Amazon Bedrock, AWS DataBrew, Amazon SES, and Amazon QuickSight

|
By Michael Oladugba, Sam Duru-Nnebue, Ferwa Zaidi
| BMC Software
The challenge with putting AI agents into production Getting an AI agent to produce a compelling response in a playground is straightforward. Getting that same agent to produce reliable, trustworthy output every quarter – on fresh data, in the right sequence, with results delivered to the right people and systems – is an entirely different problem. The gap between a demo and a production AI workflow usually is not the agent itself.
Open in Who Shared Issue with article?
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