Upgrading agentic AI for finance workflows
Finance teams moved fast to plug AI agents into research, operations, and client support. Retrieval is easy. Reliable, explainable reasoning across multi-step workflows is where most systems crack.
When your inputs are unstructured memos, messy logs, and incomplete records, opacity isn't a nuisance-it's a risk. If you can't trace how a recommendation was formed, you invite fines, rework, and poor capital decisions.
Solving the opacity problem
Throwing more agents at the issue often adds complexity without control. What matters is orchestration and the ability to inspect the full chain of thought behind every step, not just the final answer.
Sentient's new platform, Arena, tackles this head-on. It recreates real corporate workflows, feeds agents incomplete and conflicting inputs, and records full reasoning traces so engineering teams can debug failures over time.
Julian Love, Managing Principal at Franklin Templeton Digital Assets, said: "As companies look to apply AI agents across research, operations, and client-facing workflows, the question is no longer whether these systems are powerful or if they can generate an answer, but whether they're reliable in real workflows.
"A sandbox environment like Arena - where agents are tested on real, complex workflows, and their reasoning can be inspected - will help the ecosystem separate promising ideas from production-ready capabilities and boost confidence in how this technology is integrated and scaled."
Himanshu Tyagi, Co-Founder of Sentient, added: "AI agents are no longer an experiment inside the enterprise; they're being put into workflows that touch customers, money, and operational outcomes.
"That shift changes what matters. It's not enough for a system to be impressive in a demo. Enterprises need to know whether agents can reason reliably in production, where failures are expensive, and trust is fragile."
Who's putting it to work
Institutional interest is strong. Partners include Founders Fund, Pantera, and Franklin Templeton, which manages more than $1.5 trillion. Early participants also include alphaXiv, Fireworks, Openhands, and OpenRouter.
What finance leaders actually need
Repeatability, comparability, and model-agnostic reliability tracking. Platforms like Arena give engineering leaders a way to pressure-test agents against messy reality, then ship improvements with confidence.
This approach pairs well with open-source stacks. You can adapt agent capabilities to private data while maintaining audit trails, versioned prompts, and a durable reasoning record.
The integration bottleneck
Ambition outpaces governance. While 85% of businesses want to operate as agentic enterprises-and nearly three-quarters plan to deploy autonomous agents-fewer than a quarter have mature frameworks to manage them.
The average enterprise already runs about twelve agents, often in silos. Sentient contributes open-source coordination frameworks like ROMA and the Dobby model to help unify workflows and reduce operational drag.
A practical playbook for CFOs, COOs, and heads of compliance
- Map high-stakes workflows (research, compliance, client ops). Define failure modes and the review process for material decisions.
- Require full reasoning trace logging, versioned inputs/outputs, and dataset snapshots. Set retention aligned to policy.
- Adopt model-agnostic metrics: task success rate, step-to-step consistency, explanation coverage, auditability SLA, and time-to-detect/time-to-fix.
- Red-team with ambiguous and conflicting inputs. Gate deployments through a sandbox like Arena before production.
- Stand up human-in-the-loop checkpoints for portfolio moves, compliance flags, and client-impacting actions.
- Centralize agent registry and routing. Limit agents to a common orchestration layer and enforce least-privilege data access.
- Align governance to recognized guidance such as the NIST AI Risk Management Framework and banking model risk practices like OCC/Fed SR 11-7.
- Measure ROI on cycle time, error rate, rework cost, regulatory exceptions, and customer impact-not just "accuracy."
- Lock down security: PII handling, secrets management, and egress controls for prompts and reasoning logs.
Why this matters now
Agents are touching money, customers, and operations. Failures are expensive, and trust takes time to earn back. The answer isn't more demos-it's disciplined testing, traceability, and governance that travels with the workload.
For ongoing guidance on applying and governing agentic systems in finance, explore AI for Finance. Finance chiefs building a roadmap can also review the AI Learning Path for CFOs.
Key takeaways
- Don't ship agents that can't explain themselves-traceability is your control surface.
- Test on messy, real workflows before production; correctness alone is not enough.
- Standardize metrics and governance so improvements are comparable across models.
- Unify orchestration to reduce siloed agents, duplicated effort, and audit gaps.
- Treat reasoning logs as first-class data assets for audits, tuning, and training.
Your membership also unlocks:
