Org Context & AI Agents Research

Thanks for taking the time. The purpose of this survey is to understand how teams handle organizational context across modern work — both for the people tracking decisions and commitments across busy weeks, and for the engineering teams trying to give AI agents the context they need to be useful on real internal workflows. The survey routes to the questions that match your role.

This is a research project, not a sales conversation — no pitch in this survey. Your individual answers stay private; I'll only share aggregated patterns, no names or quotes attributed to you without explicit permission.

About 5 minutes. Short answers are fine — a sentence or two each is plenty. If you're interested in seeing the insights I deduce from this research, drop your email at the end and I'll happily share them once all the surveys are in.

Which best describes your role?

Technical SME / Engineering / AI Platform leader

Knowledge worker / Meeting-heavy professional

Which best describes your level?

Executive / Founder / C-suite — top of org, sets strategy, P&L authority (CEO, CTO, CFO, "Head of," founders)

Senior Leader — multi-team or domain scope (Director / ED / MD-equivalent); may or may not have direct reports

People Manager / Team Lead — manages direct reports, owns team output

Senior IC — no direct reports, owns work product, often informally leads

Individual Contributor — early-to-mid career, accountable for own deliverables

New Hire / Early Tenure — <6 months at this org, regardless of level

How large is your organization (headcount)?

Pick one AI agent or AI-powered workflow your team has worked on in the last 90 days. What was the agent supposed to do, and who was supposed to use it?

Could be a customer support agent, internal knowledge Q&A bot, a coding agent, an SDR enrichment bot, a policy lookup tool — anything you've tried to deploy.

When your team builds AI agents that need organizational context, what's harder: giving the agent reliable access to one specific thing (a document, a policy, a decision log), or giving it access to multiple related things that connect across sources (a meeting transcript and the follow-up email, a policy doc and its enforcement history, several threads on the same topic)? What's the source of that friction?

When you need to find something to do your work, what's more frustrating: pulling up one specific thing (a decision, a document, a thread), or piecing together multiple related things across sources (a meeting and the follow-up email, an email and a doc, several threads on the same topic)? What makes that hard?

What workarounds has your team built or accepted to compensate for what the agent doesn't know?

Examples: human-in-the-loop checks, scoping the agent to narrow use cases, loading context manually, deferring agent rollout.

What workarounds have you built that you wish you didn't have to?

Examples: writing recap emails just to confirm what was decided, taking notes you'd rather not take, double-checking decisions in side chats.

When your team has tried tools or approaches for agent context, what's been a deal-breaker — what specifically caused you to abandon a solution or stop trying?

Could be vendor lock-in concerns, integration overhead, output reliability problems, cost economics, security blockers, or specific things you've tried that didn't pan out.

How often do decisions, action items, or important context get lost between people or across projects in your work? When it happens, what's usually behind it?

Could be context buried in old threads, conflicting versions of "what was decided," no clear owner, decisions made in side channels, or anything specific to how your team coordinates.

(Optional) If you had a magic solution for the agent-context problem ready tomorrow — what would change about how your team ships AI agents?

(Optional) If this part of your work just worked — decisions and commitments never got lost, context was always there when you needed it — what would feel different about your week?

Setting aside whether your team would buy a tool like this — what would have to be true for it to still be in daily use six months later, after the initial rollout?

Would you like a copy of the aggregated patterns once I'm through the cohort?

Yes