When Lendi Group launched Project Aurora on AWS, they bet big on a single super-agent with 270 tools. It floppedtechnically impressive but commercially useless. The agent couldn't sell. The breakthrough came from treating AI like a workforce: specialist agents with clear roles, sales logic embedded in the funnel, and relentless measurement. Engagement tripled. This talk shares the hard lessons from Lendi's 16-week sprint: why capability isn't outcome, why your best prompt engineer might be a 23-year-old closer, and how to architect agentic systems that actually convert.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Lendi launched a single super-agent with 270 tools. It flopped — technically impressive but commercially useless. The agent couldn't sell. Pivoted to a workforce model: specialist agents with clear roles, sales logic embedded in the funnel. Engagement *tripled*.
Why it matters
This is the most important anti-pattern in agentic AI right now. Capability ≠ outcome. The "monolithic super-agent" trap is everywhere.
The hard parts
Specialist architectures need orchestration. Naive single-agent designs lose specialisation; over-decomposed designs thrash on coordination.
Playbook moves
(1) Decompose by *role*, not by tool. Each agent has a job, not just a toolkit. (2) Embed domain logic in the funnel, not just in prompts. (3) Measure on outcomes (conversions), not capability metrics (tool use).
The surprise
"Your best prompt engineer might be a 23-year-old closer." The lesson is that *domain expertise* (sales, legal, customer service) matters more than ML expertise when designing customer-facing agents. Hire from the domain, train them on the AI. Most teams have this hiring rule backwards. This may be the most quotable insight of the entire summit.
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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.
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