Key Takeaways
-
AI agents differ from traditional chatbots by reasoning through complex problems, calling tools for real-time data, and executing policy-aware actions with full auditability.
-
Kyriba’s agentic AI, TAI, combines frontier LLMs, secure orchestration frameworks, and least-privilege tool access to deliver speed, compliance, and trust in treasury operations.
-
Despite their power, LLM-based agents have limitations like context window constraints, hallucinations, and math inaccuracies, which Kyriba mitigates through grounding in tool outputs and human approvals.
At Kyriba, we deliver innovative, secure solutions to ensure customers thrive. With a proven record of practical, effective AI‑powered capabilities, we’ve consistently applied cutting‑edge technology to address the evolving needs of our customers. Recognizing the transformative potential of Large Language Models (LLMs), we introduced our agentic AI, TAI. A common question we hear is: how does it actually work?
To help demystify AI agents for treasury and finance, we’ve developed this guide to explain how they function and why understanding their capabilities—and limitations—matters for your organization. We walk through what an agent can do and how the right approach maintains the trust, compliance, and control that treasury and finance operations demand.
What makes AI agents different: Unlike chatbots that simply respond with pre-trained knowledge, agents can reason through complex problems, call tools to gather real-time data, and recommend or perform actions.
Explore related questions
Why treasury operations are perfect for AI agents: Treasury operations hold the richest, most fragmented financial data in the enterprise, and treasury decisions are time-sensitive, data-intensive, and governed by clear policies. Whether managing global liquidity, analyzing FX exposure, or optimizing cash positions, agents excel at aggregating multi-system data instantly and proposing actions with proper approvals and audit trails, translating to basis-point yield improvements and hours returned to treasury teams.
The Kyriba approach: Our agentic AI, TAI, is embedded within our platform, respecting role-based permissions, leaving complete audit trails, and ensuring every action follows the security and compliance frameworks you’ve already established. It’s designed as a policy-aware teammate that explains its reasoning and proves what it did.
What TAI delivers from day one: TAI allows treasurers to improve forecast accuracy with earlier visibility into pattern shifts; optimize cash positioning to capture yield on balances that would otherwise sit idle; and accelerate exception resolution—cutting investigation time from hours to minutes.
TAI’s Trust Foundation
-
Privately hosted on Kyriba’s secure infrastructure
-
No customer data used for training
-
Role-based access control enforced
-
Complete audit trails for every action
Permissions, privacy, and control (non-negotiables)
Trust, security, and control are at the core of Kyriba’s AI agent. In finance, where decisions are high-stakes and data is sensitive, these principles are non-negotiable:
-
Data privacy: No customer data is used in training public models. Ever.
-
Role-based access: The agent can only see and do what you are authorized to see and do in Kyriba.
-
API scopes & least privilege: Each tool is strictly scoped (e.g., read payments vs. create payments). Sensitive actions always require explicit approval.
-
Data residency & logging: All calls are fully auditable. The agent passes only structured, relevant snippets to the LLM (not full datasets). Reasoning steps are transparently traced and visible to the user in the “Thinking Steps,” including which tools were called. Machine-generated audit trails mapped to policies and logs of segregation-of-duties significantly shorten audit preparation time and close cycles.
-
Human-in-the-loop: The agent recommends; you decide. Approvals can be enforced at any action level (e.g., payments, transfers, FX), ensuring full control.
-
Reversibility: Agent actions are reviewable and reversible within policy, ensuring an additional safety layer for treasury operations.