In aviation there is a rule, even just 1 degree off in your heading over one hour will mean you will 60 miles off from your target. This demonstrates how small errors up front compound over time.
If you have even just a slightly off ICP definition that you use feed into your workflow to classify all your call transcripts, now all of those transcripts are tainted.
And lets say you don’t notice (visibility is very hard. You trade visibility for leverage in these systems), and then have another series of agents analyze these transcripts to extract pain points and assign them to ICPs to inform positioning.
The error compounds. It gets even worse.
And since we’ve all seen how AI will always yes-man you, being so confidently wrong it is laughable, it is nearly impossible to detect at the end of the run.
Ambiguity compounds into dangerous errors downstream. An unreasonable level of clarity is required to properly wield the unguided leverage that are multi-agent systems.
This is why effective context management is non-negotiable.
end to end attribution: Each new file that is created from a previous file has to link to that file. This creates an audit chain, and also a real knowledge graph
high quality human created context: Call transcripts, conversations, internal documentation, etc. Everything I can get my hands on. These are used as the source and as a way to evaluate all downstream knowledge
mutli-layered verification. Agents verify other agent work. Deterministic (regular code) checks file sizes and other heuristics to create a series of quality gates that eliminate the worse failure modes (confidently wrong on things you cannot be wrong on). And finally good old hand review by a real person SME
The result is an actionable high quality, verifiable knowledge base that makes it so:
You never have to solve the same problem twice. no more copy and pasting endlessly into ChatGPT. You painfully craft your brand tone of voice until its just right, you can use it over and over again, literally just @ it
Drastically cut time to market. Your iteration tempo flies through the roof. New content campaigns, outbound, everything that you currently do as knowledge work in GTM benefits from having a centralized knowledge base
Unreasonable Visibility & Intelligence for strategic decision making. All that insight in your hundreds of call transcripts is now queryable by a system that has the context. It can take your simple query, enrich it with existing context to go beyond the basic ask and solve for the intent of the question itself.
In aviation there is a rule, even just 1 degree off in your heading over one hour will mean you will 60 miles off from your target. This demonstrates how small errors up front compound over time.
If you have even just a slightly off ICP definition that you use feed into your workflow to classify all your call transcripts, now all of those transcripts are tainted.
And lets say you don’t notice (visibility is very hard. You trade visibility for leverage in these systems), and then have another series of agents analyze these transcripts to extract pain points and assign them to ICPs to inform positioning.
The error compounds. It gets even worse.
And since we’ve all seen how AI will always yes-man you, being so confidently wrong it is laughable, it is nearly impossible to detect at the end of the run.
Ambiguity compounds into dangerous errors downstream. An unreasonable level of clarity is required to properly wield the unguided leverage that are multi-agent systems.
This is why effective context management is non-negotiable.
end to end attribution: Each new file that is created from a previous file has to link to that file. This creates an audit chain, and also a real knowledge graph
high quality human created context: Call transcripts, conversations, internal documentation, etc. Everything I can get my hands on. These are used as the source and as a way to evaluate all downstream knowledge
mutli-layered verification. Agents verify other agent work. Deterministic (regular code) checks file sizes and other heuristics to create a series of quality gates that eliminate the worse failure modes (confidently wrong on things you cannot be wrong on). And finally good old hand review by a real person SME
The result is an actionable high quality, verifiable knowledge base that makes it so:
You never have to solve the same problem twice. no more copy and pasting endlessly into ChatGPT. You painfully craft your brand tone of voice until its just right, you can use it over and over again, literally just @ it
Drastically cut time to market. Your iteration tempo flies through the roof. New content campaigns, outbound, everything that you currently do as knowledge work in GTM benefits from having a centralized knowledge base
Unreasonable Visibility & Intelligence for strategic decision making. All that insight in your hundreds of call transcripts is now queryable by a system that has the context. It can take your simple query, enrich it with existing context to go beyond the basic ask and solve for the intent of the question itself.
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