Introduction: a new posture for AI
We are moving from a world where AI sat passively behind interfaces to one in which software systems act as proactive, goal-oriented participants in workflows. These systems—commonly called AI agents or agentic AI—combine large language models, planning, tool use and API orchestration to perform multi-step tasks with degrees of autonomy. Industry reports and vendor roadmaps place agentic systems among the most-discussed enterprise trends in 2024–25, with rapidly growing adoption in information-heavy industries such as banking and capital markets (see LangChain State of AI Agents and Deloitte’s guidance on autonomous generative AI) (https://www.langchain.com/stateofaiagents; https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/autonomous-generative-ai-agents-still-under-development.html).
For banking professionals and business analysts, this shift is less about replacing human judgment and more about amplifying it: turning slow, manual, exception-prone processes into scalable, auditable, and faster services. The rest of this article explains what AI agents are, why banks care, and how practitioners—especially those with responsibilities around APIs, data and process design—can lead responsible adoption.
What exactly is an AI agent (and how is it different from traditional automation)?
At a practical level, an AI agent is a system that: (1) receives high-level goals or instructions in natural language, (2) plans multi-step activities, (3) chooses and interacts with tools (APIs, databases, RPA, search, calculators) and (4) monitors, adapts and reports outcomes. Key differences from older automation approaches:
- Autonomy and planning: modern agents can chain reasoning steps and plan actions across multiple tools instead of executing a fixed script.
- Tool awareness: agents can discover and call APIs, use internal analytics, query knowledge bases, or delegate to other services as needed.
- Language-first interface: stakeholders often instruct agents in business language rather than precise technical scripts, making them accessible to analysts and business stakeholders.
- Observability and human-in-the-loop: while agents can act, best practices embed approval gates, audit logs and rollback controls so humans retain ultimate control.
These capabilities let agents handle complex, exception-rich workflows—e.g., reconciling messy payment data or triaging compliance flags—tasks that were previously expensive to scale with hand-coded pipelines or RPA alone (see LangChain State of AI Agents report for common patterns) (https://www.langchain.com/stateofaiagents).
Why banks are adopting AI agents — measurable wins and representative use cases
Banks are natural early adopters because they run many information-heavy, rules- and exception-prone processes where small efficiency gains scale into material cost savings and better client experiences.
Real-world example: Scotiabank prototyped agentic automation for its Client Insight Report and payment operations by partnering with EY and Microsoft. The bank built a team of five specialized agents (data transformation, financial reconciliation, exception handling, aggregator, and report preparation) on Microsoft’s Copilot platform in under three months. The result: what previously took weeks of manual effort can now be generated in seconds, enabling delivery at scale and creating new monetization paths for formerly bespoke services (Scotiabank Client Insight Report case study) (https://gtb.scotiabank.com/en/global-transaction-banking/resources/insights/article.insights.how-ai-agents-are-transforming-scotiabank-s-payment-operations.html).
Other high-impact use cases in financial services include:
- Autonomous fraud detection and response, where agents correlate signals and kick off containment actions (IBM’s financial services guidance provides examples) (https://www.ibm.com/think/topics/ai-agents-in-finance).
- Intelligent credit underwriting and pre-screening that synthesizes alternative data and produces explainable recommendations.
- Automated regulatory reporting and audit-prep by gathering, reconciling and annotating data across systems.
- Trade surveillance, exception triage and post-trade reconciliations where agents organize the investigation and draft human-readable summaries.
Market context: the autonomous AI and agents market was reported to exceed USD 6.8 billion in 2024 and market analysts project high compound annual growth rates (e.g., ~30%+ CAGR in some forecasts) as firms operationalize agentic capabilities across workflows (industry market reports) (https://www.gminsights.com/industry-analysis/autonomous-ai-and-autonomous-agents-market).
Where business analysts and API-focused practitioners add the most value
If you are a business analyst, product owner, or API practitioner—roles Sylvia Yu’s LinkedIn activity suggests you follow—you are uniquely positioned to translate business value into agentic solutions. Here’s a practical path you can follow when scoping and delivering agents inside a bank.
- Start with information-heavy, high-exception workflows
- Pick processes that are manual, slow, and rely on unstructured or inconsistent data (payments reconciliation, client insight reports, compliance investigations). Scotiabank’s five guiding principles—focus on information-heavy workflows, unlock constrained value, elevate client experience, enable auditability, and design with energy awareness—are a useful prioritization lens (Scotiabank case study) (https://gtb.scotiabank.com/en/global-transaction-banking/resources/insights/article.insights.how-ai-agents-are-transforming-scotiabank-s-payment-operations.html).
- Map APIs, data sources and tool contracts
- Document every system the agent must call (core banking, transaction stores, KYC/AML systems, reporting DBs) and capture API contracts, latency and error modes. Business analysts who already model processes and data flows are well-placed to own this mapping.
- Design modular agents and responsibilities
- Treat the overall solution as a team of agents (data cleaning, rule-based verification, exception reasoning, human summary). Modular design makes debugging, auditing and governance easier and lets you reuse components across processes.
- Specify guardrails and explainability requirements
- Define required audit trails, human approval thresholds, and the formats in which agents must justify decisions. Financial services regulators and internal model-risk teams will expect traceability.
- Prototype fast, measure outcome metrics
- Build a narrow proof-of-concept (Scotiabank’s prototype was completed in under three months) focused on speed-to-value. Track client-facing KPIs (response time, client satisfaction) and operational KPIs (cases resolved, manual hours saved, error-rate reduction).
- Operationalize with monitoring and incident playbooks
- Put telemetry on agent actions, tool calls, and confidence scores. Create incident playbooks for model drift, data outages and high-severity false positives.
Practical architecture sketch
- Orchestrator (agent manager/dispatcher)
- Tool layer (APIs to payments ledger, KYC DB, fraud engine, RPA bots)
- LLM/Reasoning layer (planning, prompts, chain-of-thought)
- Audit & explainability layer (immutable logs, provenance metadata)
- Human-in-the-loop interface (approvals, edits, final sign-off)
Business analysts who understand APIs can bridge front-line requirements and engineering teams, ensuring agents call the correct services and that outputs are consumable by downstream systems.
Governance, risks and responsible adoption
Agentic AI raises specific operational and regulatory risks for banks. Key risk areas and mitigations:
- Data privacy and residency: ensure agents only access and store data consistent with internal policies and local regulation. Apply data minimization and tokenization when needed.
- Model risk and explainability: require provenance for decisions (which API calls, which documents, what rules applied). Embed human approval for high-risk decisions and maintain version control for models and prompts.
- Security and supply chain: secure API keys, isolate agent runtimes, and vet third-party libraries and services used by agents.
- Compliance and audit: design agents so every action is logged, timestamped and tagged with the responsible model/version and human approver when applicable.
- Ethical and fairness considerations: monitor for biased outputs in decisions that affect customers (e.g., credit, pricing), and run fairness checks as part of pre-deployment tests.
Vendor and partnership strategy
- Many banks choose to prototype with cloud and systems integrator partners (Scotiabank worked with Microsoft and EY), then harden solutions into internal platforms as confidence grows. Partnerships accelerate time to prototype but require careful vendor risk assessment and contractual clarity around data use (https://gtb.scotiabank.com/en/global-transaction-banking/resources/insights/article.insights.how-ai-agents-are-transforming-scotiabank-s-payment-operations.html).
Getting started: a checklist for the first 90 days
- Identify one high-value, information-heavy workflow and secure a stakeholder sponsor.
- Map required APIs and data owners; list required SLAs and authorization scopes.
- Define success metrics: time saved, client reach, error reduction and auditability targets.
- Build a narrow prototype (1–3 modular agents) and test with synthetic and historical data.
- Run red-team and compliance reviews before any external-facing rollout.
- Instrument monitoring and define escalation paths for model/operational incidents.
Conclusion: amplify, don’t replace
AI agents will not render human analysts obsolete; they magnify the best parts of human judgment—context, ethics, nuance—by removing routine work and delivering timely insights. For business analysts and API-savvy practitioners in banking, that creates an opportunity to lead: map value, design agent responsibilities, and bake governance into solutions from day one. When deployed responsibly, agentic AI can turn boutique, bespoke services into scalable capabilities that preserve trust and improve both client outcomes and operating economics (Scotiabank’s Client Insight Report is a blueprint for this transition) (https://gtb.scotiabank.com/en/global-transaction-banking/resources/insights/article.insights.how-ai-agents-are-transforming-scotiabank-s-payment-operations.html).
Relevant resources
- LangChain — State of AI Agents (2024): https://www.langchain.com/stateofaiagents
- Scotiabank — How AI Agents are Transforming Payment Operations (case study): https://gtb.scotiabank.com/en/global-transaction-banking/resources/insights/article.insights.how-ai-agents-are-transforming-scotiabank-s-payment-operations.html
- Deloitte — Autonomous generative AI agents: https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/autonomous-generative-ai-agents-still-under-development.html
- IBM — AI agents in finance: https://www.ibm.com/think/topics/ai-agents-in-finance
- Market analysis — autonomous AI and agents market (growth estimates): https://www.gminsights.com/industry-analysis/autonomous-ai-and-autonomous-agents-market
If you’d like, I can convert the 90-day checklist into a one-page project brief or a template you can use with stakeholders and data owners at your bank.