# A Bank Built Its Own Coding Agent. It Took Off — Felipe Felisola Caso & Francesco Galletta, Monzo

## Executive summary

Monzo details the successful development of 'Agent Chip,' an in-house AI coding agent that has become a core component of their engineering platform. Instead of focusing solely on the LLM technology itself, the key success factor was building a robust internal platform—the 'lots of little multipliers'—which included an MCP gateway, a sandboxed agent orchestration system, and the ability to treat the agent as a composable building block. The agent now authors over 10% of all merged PRs and runs over 1,800 tasks daily, enabling automation in areas like incident response, data quality monitoring, and allowing non-specialists to contribute code drafts.

## Key takeaways

- Focus on Multipliers, Not Just LLMs: The success of the agent was enabled by pre-existing platform capabilities (e.g., alerting, monitoring, staging environments, queuing abstractions) rather than just the LLM itself. These multipliers allowed the agent to function effectively.
- Composability as a Building Block: By treating the agent as a composable building block, Monzo can connect it to various internal systems (Slack, GitHub, Linear, and over 3,000 services), driving value across product features and business outcomes.
- Controlled Agentic Development: The architecture uses a sandboxed, isolated namespace for remote agents, ensuring that the non-determinism of the AI is controlled and managed within a regulated banking environment.

## Technical details

- MCP Gateway: The Multi-Context Platform (MCP) gateway was developed to provide easy, controlled access to internal knowledge sources and services. It manages authorization and authentication, allowing agents to connect safely to internal tools.
- Agent Orchestration and Sandboxing: The agent operates within an isolated namespace and sandbox, which is crucial for a regulated bank. The architecture uses an agent image to define exact tools and access (CLIs, environment variables), and a proxy to manage egress, ensuring controlled access to repositories and code.
- Agent Use Cases: The agent is used for advanced tasks including: 1) Incident Mode (understanding logs, traces, and connecting to services during an incident); 2) On-Call Data Quality (observing failures and triggering investigations); and 3) Proactive Monitoring (identifying potential data issues from back-end changes before batch execution).

## Practical implications

- The agent lowers the barrier to entry, allowing non-specialists to draft working code based on product feedback, which can then be reviewed and deployed by an engineer.
- The platform is provider agnostic, meaning the core agent box can be easily swapped by any LLM provider, offering flexibility.
- The system can cover a broader development window, allowing end-to-end testing from drafting back-end changes to testing in a staging environment.

## Topics

AI Agents, LLMs, Software Architecture, DevOps, Financial Technology, Monzo

Source: https://www.youtube.com/watch?v=adfw3xJBx1o