Building an Enterprise-Grade Agentic Framework for Financial Institutions

Agentic AI is generating enormous attention but also skepticism. Gartner predicts that over 40% of agentic AI projects will be cancelled by 2027 due to costs, unclear ROI, and inadequate risk controls. Many initiatives today are still hype-driven pilots, with only a fraction showing true enterprise value.

And yet, the long-term potential is undeniable. By 2028, 15% of day-to-day work decisions could be made autonomously through agentic AI, and a third of enterprise software will embed agentic capabilities.

The question for banks and financial institutions isn’t if agents will matter, but how to adopt them responsibly and at scale.

Agents Are Mushrooming -But Finance Plays by Different Rules

AI agents are spreading across industries -running marketing campaigns, code reviews, and customer support workflows. The hype is simple: agents will replace workflows and reshape industries.

But finance is different. A wrong credit decision, a missed AML flag, or an error in compliance reporting can mean regulatory fines, systemic instability, or reputational damage. For banks, credit unions, and financial institutions, the real question is not what agents can do, but what they must prove to earn trust.

Why BFSI Adoption Is Different

The financial industry carries unique weight compared to other sectors:

• Regulatory Pressure: From the EU AI Act and GDPR to OCC (US), MAS (Singapore), RBI (India), APRA (Australia), BSP (Philippines), DFSA (UAE), SAMA (Saudi Arabia) -regulators demand transparency and accountability.
• Zero Tolerance for Error: Even a 1% error could mean billions in fines or losses.
• Customer Trust as Currency: Banks don’t just manage money -they manage credibility.
• Culture of Service: Banking is about fairness, empathy, and inclusion, not just efficiency.

10 Pillars of an Enterprise-Grade Agentic Framework

1. Ethical AI by Design – bias detection, fairness audits, ESG alignment.
2. Guardrails & Governance – limits, escalation, human-in-the-loop.
3. Data Privacy & Security – fine-grained access, encryption, compliance.
4. Cost & ROI Alignment – cost observability, measurable ROI.
5. Infrastructure Readiness – hybrid stacks, GPU/TPU scaling, sandboxing.
6. Scalability & Resiliency – SLA monitoring, graceful fallback, kill-switch.
7. Cultural Alignment – fairness, empathy, and customer-first tone.
8. Regulatory & Audit Readiness – replayable logs, explainability dossiers, evidence packs.
9. Skill Set Requirements –
   • AI/ML engineers for orchestration.
   • Domain experts for deep banking context.
   • Governance & compliance professionals.
   • Cross-functional calibration teams.
10. Self-Learning with Guardrails – adaptive loops that improve accuracy while staying within compliance and cultural norms.

Agentic AI: More Than a Technology -A Framework for the Enterprise

Agentic AI is not just another technology stack. It dynamically interacts with multiple layers of a bank’s ecosystem -from lending and payments to compliance, risk management, and customer service.

To succeed, it requires:

• Enterprise-Level Understanding -aligned to operating models, risk appetite, and commitments.
• Business Domain Depth -AML, fraud, lending, trading need contextual intelligence.
• Ecosystem Connectivity -orchestration across core banking, CRMs, risk engines, and regulatory tools.
• Operational Uplift, Not Disruption -efficiency gains without new risk vectors.

In short, Agentic AI is a framework, not a feature.

How Regulators Are Watching the Agentic Shift

Regulators worldwide are tightening oversight:

• EU: AI Act classifies finance as high-risk; DORA mandates resilience & red-team testing.
• US: OCC, FDIC, Fed apply SR 11-7; NIST AI RMF is becoming global reference.
• UK: PRA PS6/23 assigns board-level accountability for AI/ML.
• Singapore: MAS FEAT principles & Veritas Toolkit.
• India: DPDP Act (privacy) and RBI digital lending rules.
• Australia: APRA CPS 230 (operational risk) & CPS 234 (security).
• Philippines: BSP stresses consumer fairness, protection, and cybersecurity.
• Middle East: UAE (DFSA), Saudi (SAMA, SDAIA) require auditability, Shariah compliance, and customer safeguards.

What’s next? Expect “agentic stress tests” - bias testing, failover drills, explainability audits -much like today’s capital adequacy tests.

The Investment Context (2025 and Beyond)

• $35B spent in 2023, with $21B from banking alone.
• $97B projected by 2027, fastest AI growth across industries.
• 78% of organizations using AI in 2025, up from 55% in 2024.
• 44% BFSI adoption of agentic AI by 2026, a sixfold increase.

But investment is not just software:

• GPU & Compute Readiness -local GPU clusters + cloud partnerships.
• Model Strategy -LLMs for broad reasoning, SLMs for faster, cheaper domain depth.
• Scaling Costs -thousands of concurrent agents drive orchestration & compute overheads.
• Infrastructure Resilience -redundancy, failover, energy-efficient ops for compliance.

Roadmap for CXOs

Ask the Right Questions

• What guardrails govern decisions?
• How are actions audited and explained?
• What happens when agents fail?

Tier Use Cases by Risk Appetite

• Low-risk → reporting, reconciliations.
• Medium-risk → compliance triage, loan pre-screening.
• High-risk → lending decisions, trading (always with oversight).

Adopt Crawl-Walk-Run

• Crawl: Co-pilot/advisory agents.
• Walk: Semi-autonomous with strict controls.
• Run: Autonomy only with proven trust, resilience, regulatory approval.

Closing Thought

For financial institutions, trust is the true currency. Agentic AI will not win adoption through hype but through ethics, privacy, governance, cost discipline, resilient infrastructure, and regulatory readiness.

The future of enterprise-grade frameworks will be defined not by how many tasks agents can do -but by how confidently boards, regulators, and customers trust them to do it.

References

1. Gartner (2025). Gartner Predicts Over 40% of Agentic AI Projects Will Be Cancelled by End of 2027. Link
2. McKinsey (2024). The state of AI in 2023: Generative AI’s breakout year. Link
3. World Economic Forum (2024). AI Regulation Tracker. Link
4. NIST (2023). AI Risk Management Framework. Link
5. BIS (2024). Fintech and AI in Banking Reports. Link