I'd like to experiment with 'paired' persona context engineering in my next position managing an objective…
Steven D. — Consultant
I'd like to experiment with 'paired' persona context engineering in my next position managing an objective or mission such as an InfoSec IAM modernization program. What this means is standing up a side-by-side team of AI personas to perform the same InfoSec program functions the RL team is doing. Of course, my employer would have to be willing and have the organizational resources in place to support it.
Out of the gate, this would be sensitive enough to require data-driven persona integrity and security. This means the personas' training data and the generated responses from their work have to be securely managed and stored along with all involved networks and infrastructure. I would also want to leverage containerization to enable better visibility and control on many levels -> resource utilization and performance, persona behavior fine tuning, and persona teaming and collaboration techniques. Supporting functions like governance, regulatory compliance, and audit could be additional personas stood up to round out the effort.
The opportunities and innovations that come to mind for me are nearly endless; way beyond IAM and InfoSec. First of all, I am just one person and I don't have the infrastructure and resources to experiment with this by myself. I need an organization and a team of SMEs to collaborate with. My thought experiment is firmly in the clouds. The AI persona teams can be tailored to work with each other and interact with RL in novel ways both horizontally and vertically. Lastly, expanding the RL interactions beyond LLM to include sensors (eg. multi-modal) presents an unlimited horizon of use cases.
7 reactions · 4 comments
Comments
Schomer: Fascinating paradigm, Daignault. If you are executing a side-by-side AI persona framework for high-stakes missions like IAM modernization, your primary threat vector isn't standard perimeter access—it's semantic drift and prompt manipulation between the agents themselves. Relying on an LLM or an AI persona's internal alignment to maintain security boundaries is an enterprise gamble. The second an
Steven D.: Hi. Thank you for the feedback! This specific example, IAM modernization, has more to do with me since that happens to be the work I'm performing. The AI objective is developing some competency with persona integrity and security and more generally with teaming and collaboration techniques between AI roles. The AI training performance expertise being sought is many fold and dependent on (AI) trial
Steven D.: Schomer I liked learning more about layer zero lightweight, plug-and-play validation engines from your posts and elsewhere. Assuming I am understanding DVNs and your ideas well enough, it definitely plays into my AI persona integrity concerns as a cornerstone of a security strategy for a mission that is inevitably going to be autonomously performed.
Schomer: Steven, you’ve hit the exact nail on the head. When an architecture transitions from pure LLM text generation to downstream physical execution, sensors, and multi-modal payloads, relying on an agent's internal state for safety boundaries transitions from an application risk to a physical liability. A probabilistic AI persona needs room to make mistakes, context-switch, and explore unknown states t