Why Configurable AI Agents Matter
No two lenders work the same way. Credit policies set different thresholds, credit memos follow different templates, spreads use different charts of accounts, and approval authorities vary by institution, product, and loan size. An AI agent that only works one way forces the institution to change its process to fit the software, or leaves staff reworking its output.
At the other extreme, a fully custom-built agent can match every detail, but it takes longer to build, costs more to maintain, and becomes harder to update as policy changes. Configurable AI agents sit between the two. The core capability, such as document extraction, spreading, or memo drafting, is built and tested once. Each institution then configures how it applies: its templates, rules, thresholds, and workflow.
When policy changes, the configuration changes. The agent does not need to be rebuilt.
Configuration lets an agent work the way your institution works, while keeping the tested core of the agent the same for every customer.
What Can Be Configured
- Policies and thresholds: ratio limits, exception rules, and credit policy checks.
- Templates and formats: credit memo structure, spread layouts, and report formats.
- Data sources: which systems and documents the agent uses and in what order.
- Workflow and approvals: routing rules, approval authorities, and when human review is required.
- Outputs and write-back: which fields and systems receive the agent’s results.
- Product variations: different settings for CRE, C&I, SBA, equipment finance, or consumer lending.
Configurable vs Fixed vs Custom-Built Agents
| Dimension | Fixed agent | Configurable agent | Custom-built agent |
|---|---|---|---|
| Fit to institution | Low | High | Very high |
| Time to deploy | Fast | Fast | Slow |
| Change effort | Vendor release needed | Configuration change | Development work |
| Maintenance | Vendor-managed | Shared: vendor core, institution settings | Institution or contractor |
| Validation | Same for everyone | Core validated, configuration tested | Full validation each time |
How Configuration Works in Practice
- Start from a prebuilt agent: choose an agent for a defined task, such as spreading or covenant testing.
- Apply institution settings: load templates, policies, thresholds, and workflow rules.
- Test on real files: run the agent on historical deals and compare results with prior work.
- Approve and release: move the configuration to production under change management.
- Adjust over time: update settings as policy, products, or regulations change, with each version recorded.
Governance of Configuration
Configuration is powerful, so it needs the same discipline as any other change to a decision process. Institutions should control who can change settings, test changes before release, keep a version history, and document how configuration affects outputs used in credit decisions. That supports model risk management, fair lending review, and audit.
How Uptiq Configures Its AI Agents
Uptiq’s Qore agents are configured to each institution’s own policies and formats, for example generating credit memos in the institution’s own template, and connect to existing systems through 100+ integrations. A single agent can typically go live in 5 business days and a full suite in 30 days. Across more than 150 financial institutions, teams using Qore have seen 41% faster underwriting and 63% less credit memo prep time.
Frequently Asked Questions
What are configurable AI agents?
What can be configured in an AI agent for lending?
How are configurable agents different from custom-built agents?
Do configuration changes need to be governed?
Can configurable AI agents support multiple loan products?
Talk to an expert about Uptiq agents that work the way your institution works.
