NEW Top 10 Highlights Critical Steps in Developing Agentic AI Agents

Editor Note: This video was written and produced to “Fix the Agentic AI design flaws” from the more than 200+ companies that were produced into video news stories available on ChannelPartner.TV.
IMPORTANTLY, ALL companies missed a number of key concepts needed for high-performance, risk-less and user-centric AI systems. Click on image for Video
Bottom-line – Fix these issues before you buy-build or do AI:

1 – Consider starting with new “greenfield” problems, rather than trying to fix a legacy problem that can’t be fixed, or very difficult to be improved upon. Or as one person said “If you automate a bad process, you just get bad results faster.”
2 – Capture Institutional Knowledge – colleagues who know systems and processes better than anyone else, so before they leave or retire ensure “human intelligence” data is captured, including all levels in the organization.
3 – Cleanse – data gathering is a continuous cleansing process, along with remembering “garbage in = garbage out”, as data parameters continuously change, with ever-evolving data fields, data taxonomies, ontologies along with meta tagging, and as cross organization data points are integrated.
4 – Condense – raw, even structured data, needs to be organized or condensed into human usable processes already in use. Develop a methodology or algorithm, that is understandable and practical by the team.
5 – Collaborate – is where problems and processes are identified, and collaborated across the company, to determine usable, measurable, and actionable outcomes and financial metrics. This also is the phase to design “user use cases”, not just “use cases” to personalize agents to specific users most in “need” of the solution.
6 – Communicate – then develop a pilot app led by those “all-in”, then test for as long as it takes, continuously try to “break it” by trying any, and all possible “wild card” user interactions, including simulations.
7 – Continuous control – including benchmarks and version control, to reverse and repeat previous steps. Release to small and then add users carefully, and stop immediately with any problem. Have a crisis team on alert before public launch, including a user support group and even gamification.
8 – Contain and Kill – couple any hallucination or public crisis with a “kill switch”, to move back to previous version, identify and document problems, review results and release carefully.
9 – Comply – get third-party compliance certification, for industry and government regulations, along with a new internal AI auditing team.
10 – Create Again – means creating a new team to develop a new version, as this process has changed users and organizational structure. and being ready for an ever-evolving ever-faster approaches such as B2A–business-to-agent and agent orchestration and more.

Bottom line – AI also changes the way humans interact with other humans which changes the way humans work.

email cross@gocross.com to get help today.

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