Background and problem statement
Banks and financial institutions want to use generative AI, but most early pilots stop at chat. Ask an assistant to prepare a client brief and it explains how you could do it yourself.
The model knows a lot, but it can’t reach core systems, follow internal policy or act for the user. For relationship managers and risk teams, the manual work stays the same, now with an extra step.
Approach taken
In our projects, G-Square builds agentic capability one layer at a time: nine abilities in four parts, each tested before the next is added.
- Give it hands: tools and MCP (Model Context Protocol) connect the model to existing systems through one standard interface.
- Make it act: agents work toward a goal in a think, act and observe loop. Specialist agents work as a team, and A2A lets agents in different systems collaborate.
- Make it know us: RAG grounds answers in internal documents, while memory and skills keep context and house formats.
- Make it safe: layered guardrails, limited access and human approval for risky actions, plus an examiner that tests the agent against known answers.
Solution
The result is an agent that completes a task from a single request. In a concept simulation, a relationship manager asks: “Prepare me for my 11 AM meeting with Axe Industries.”
The agent recalls concerns from the last meeting, pulls the portfolio and open points through MCP, and checks credit policy. Specialist agents research, analyse risk and draft the brief in house format. Every figure is checked against its source, and nothing is sent without approval.
Benefits
- Time back for front-line teams: work that meant switching between several systems arrives as one ready brief.
- Built on what you already have: MCP connects existing systems instead of replacing them.
- Consistent output: skills and memory keep every brief in house format and carry context forward.
- Trust by design: guardrails, human approval and testing keep the institution in control.
Conclusion
The real value of AI in financial services comes when it moves from answering questions to completing work. Building that capability layer by layer keeps each step understood, tested and safe. With the right connections, knowledge and guardrails, an agent becomes a dependable member of the team. At G-Square, this is how we help institutions turn AI from saying into doing.
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