As AI moves from assistants to agents, success depends on contextwhat the system attends to and acts on. In this lightning talk, well show three examples of context-aware agents in the enterprise through quick demos: meeting coordination, engineering issue resolution, and sales forecasting, highlighting how the right context enables reliable execution at work.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Three quick demos: meeting coordination, engineering issue resolution, sales forecasting. Context determines reliable execution.
Why it matters
Agents without your team's context produce generic answers. Generic answers are useless.
The hard parts
Context aggregation across SaaS tools is a permission and freshness problem. Stale context is worse than no context.
Playbook moves
(1) Audit which corpora the agent has access to. (2) Refresh frequency matters more than corpus breadth. (3) Build relevance filtering before broader access.
The surprise
The hardest part of "context-aware" agents isn't access — it's *relevance*. Most enterprise data is noise to most queries. Relevance filtering is where the engineering work hides.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.