AI Publications

Public·4 members

By Dharmesh Mistry

1. The Problem: Fragmented and Static Integrations

Banks and large enterprises have traditionally built complex tech stacks by buying, building, and integrating systems in siloes.
These integrations are often clumsy, inconsistent, and hard to maintain.
There’s no universal standard for how apps should “understand” each other — leading to extensive manual documentation and developer work.

2. Emerging Shift: Agentic AI Architecture

AI is progressing from content handling (text/images) to understanding context, intent, and capability, enabling apps to autonomously discover and use each other.
LLMs already read documentation, generate code, call APIs, and could soon coordinate workflows between apps without developer input.
This transition moves from static, developer-led integration to dynamic, AI-led interaction.

3. How It Works: AI‑Powered Ecosystems

Banks can train AI to scan their internal software landscape, catalogue available APIs and endpoints, and learn from usage patterns .
Early adoption of standards like Model Context Protocol can enhance AI agents’ abilities to understand and integrate systems.
AI agents will learn from usage patterns, open APIs, and organizational context to automate workflows.
With this foundation, simple natural‑language commands (e.g., “Generate overdraft letter when balance is negative”) can trigger AI-led actions across multiple tools.

4. Transition: From Integration to Interaction

The industry could evolve from static, developer-led integration to dynamic, AI-driven interaction that adapts workflows in real time.
APIs remain critical, but AI provides semantic understanding, interpreting what needs to happen and coordinating across systems seamlessly.

5. Risks & Governance

Autonomous AI-led connections pose risks—security breaches, misconfigurations, or unintended behaviors.
Robust guardrails, policies (Agent Operating Procedures), and explainability are essential for banks to maintain control and comply with regulations .
AI actions must be transparent and traceable, enabling auditors or regulators to understand why certain decisions were made.

6. Business Value & User Benefits

Faster time-to-market: eliminates slow, human-mediated integrations.
Frees up humans for higher-value tasks.
Operational efficiency: AI agents proactively suggest optimizations and automate routine tasks.
Strategic insights: a self-optimizing ecosystem can identify potential synergies or business opportunities across apps.

7. Call to Action

The future is not distant—but banks should start small by piloting AI-enabled integrations in non-critical areas .
Investing now in documentation, better API designs, and explainability frameworks sets the stage for future-scale agentic systems .
Will you be ready to let your apps talk to each other—and create a self-building bank?