Aurora
An agent operations platform.
Define what an agent should own — then ship the runtime that lets a fintech operate it on Monday.
✺ — The problem
Aurora had eleven half-finished agent prototypes and zero in production. Each lived in its own notebook, each used a different LLM, and none of them could be audited. The board had approved AI as a strategy in Q1 and was asking for a P&L attribution by Q3.
Sector
Fintech
Year
2026
Duration
14 weeks
Team
1 Principal · 2 Engineers · 1 Eval lead
Stack
✺ — Approach
The same arc as every engagement — tuned to this problem.
Define · One week of decisions
We ran six discovery sessions across ops, risk, and engineering. Out of eleven prototypes, two had real ROI math behind them. We killed nine and wrote the brief for the remaining two.
Build · A single runtime
One agent runtime with shared memory, tool schemas, audit trails, and a human-in-the-loop escalation path. Built on a vendor-neutral foundation so model swaps cost a config change, not a rewrite.
Operate · Eval before deploy
Every agent ships with a regression suite scored against held-out historical tickets. Drift dashboards alert ops before customers notice. Friday changes ship safely because Monday's eval already ran.
✺ — Outcome
Three numbers we’d defend in public.
47%
of L1 tickets now resolved by agents
3.2×
faster time-to-prod for the next two agents
$2.1M
annualized ops savings, audited Q2 → Q4
“We came in with eleven prototypes and a slide deck. We came out with two production agents, a runtime our team understands, and a board update we were proud to write.”
VP Engineering, Aurora