Asset management runs as a layered control plane: custodian and pricing data feed a governed context layer, agents run the overnight fund work, and a human signs off before anything is published.
Asset management is not one system but a stack of layers that each hand work to the next. Sources of record such as custodian files, positions, and pricing feed a governed context layer that fixes metric definitions, fiscal periods, and entity rollups. Agents then automate the overnight work of ingesting, pricing, reconciling, and drafting a preliminary NAV, while a human stays in the loop for every decision. An assurance spine of traceability and role permissions runs through every layer, which is what makes the output defensible to investors and regulators.
Agents need approved metric definitions, fiscal periods, and entity rollups to answer questions the same way a controller would. Without that layer, plain-language queries and reconciliation drift as strategies and fee arrangements change, and answers arrive too late to guide decisions. The context layer is what keeps definitions current and shared across teams.
The agent layer produces a preliminary NAV by ingesting custodian files, pricing positions, and reconciling records, but it does not make investment or valuation decisions. A human reviews every action before anything is final. Every figure remains traceable to its source and permissioned by role, which is what makes the result provable to investors or regulators.
Score candidates on business impact, demand frequency, data readiness, scope clarity, and governance risk. Scores of 20-25 mean build now, 14-19 need shaping, and under 14 is a poor fit, with an executive champion required regardless. Narrow scope and governed data matter more than an ambitious everything-agent.
The posts behind it
Databricks · · Problem & solutionFinance teams must interpret data to guide decisions, but answering follow-up questions often requires manual extraction and metric negotiation, delaying answers past leadership discussions. Genie One is a data-smart AI coworker grounded in business context such as approved metric definitions, fiscal periods, and entity rollups, using Genie Ontology to unify context. It supports workflows including budget variance analysis, cash flow forecasting, financial close management, and spend analytics, allowing plain-language queries like “Which entities have unreconciled intercompany balances above $100,000?” Users can review tables behind answers, schedule recurring reviews, and have results posted to chat with visualizations. Genie One respects each user’s existing permissions, so a corporate controller sees enterprise-wide items while a regional accountant sees only relevant entities.
Databricks · · Problem & solutionWith over 1 million Genie Agents created in 2026, the challenge is selecting which to build first for maximum impact. The post presents a rubric scoring candidate workflows on five variables: business impact, demand frequency, data readiness, scope clarity, and governance risk. Scores of 20-25 indicate readiness to build, 14-19 need shaping, and under 14 suggest the workflow is not a good fit; an executive champion is essential regardless of score. Examples include a pension fund’s risk Q&A agent that scored high and saw quick adoption, and a wealth manager’s analytics agent that failed due to metadata gaps. The rubric helps avoid common pitfalls like the ‘everything agent’ and the ‘vanity demo’ by focusing on narrow scope and governed data.
Databricks · · Problem & solutionBuy-side asset management finance departments face growing complexity from AI agents influencing valuation, redemption, and reconciliation workflows, while protecting net fee margin and NAV integrity remains their core mission. Databricks Genie One is presented as a data-smart AI coworker grounded in Genie Ontology, which captures business meaning and keeps definitions current as strategies and fee arrangements shift. Behind the assistant, Genie Agents automate overnight fund operations: ingesting custodian files, pricing positions, reconciling records, and producing a preliminary NAV for review. Every figure is traceable to its source, permissioned by role, and provable to investors or regulators, enabling finance teams to see forming risks early. The system does not make investment or valuation decisions; a human remains in the loop for every action.
Supabase · · AnnouncementSupabase announced tools at Supabase Select that let coding agents manage backend configuration and deployment from code, eliminating the need for dashboard-only changes. Declarative Schemas 2.0 allows agents to edit SQL files as the source of truth, with pg-delta generating migrations, while config.toml holds settings like auth providers that can be pulled back from the dashboard. Local development now runs as native processes without Docker, supporting multiple instances per directory and working in environments like Claude Code’s sandbox, currently in alpha off by default. Supabase Compute provides long-running services in any language alongside the database, with configurable memory and CPU, public or private HTTP endpoints, and deployment via CLI or Management API. The new MCP server lets user agents interact with an app as the signed-in user, respecting existing RLS policies, built on @supabase/server and Supabase Middleware, with a headless app template available.
Databricks · · ExplainerTraditional Postgres point-in-time recovery is slow because compute and storage are coupled, requiring provisioning a new instance, restoring a snapshot from object storage to disk, and replaying WAL, leading to hours of downtime for large databases. In a survey of developers running 1TB+ production Postgres, 59% had a critical production failure and 30% were down for over three hours. Lakebase Postgres decouples compute and storage using safekeepers, a pageserver, and object storage, storing database history as an immutable timeline. A restore becomes a branch at a point in history, a metadata operation that does not copy data, making it near-instant regardless of database size. This eliminates data copies and WAL replay, so restoring a 100 TB database is as fast as restoring 10 GB, and agents can use restores as ordinary tool calls for versioning and undo.
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