Bridging the Legacy Gap: Multi-Layer AI Architecture for COBOL Systems Integration

Editor's Note: This article is a working draft and subject to refinement. Your feedback is valuable. Let me know what you think!

Introduction

The realm of software is a paradoxical blend of the new and the old. While emerging languages and frameworks captivate our attention, legacy languages like COBOL, FORTRAN, and even machine code remain the bedrock of crucial sectors like finance and governance. These long-standing technologies come with a price: shrinking expertise, prohibitive migration costs, and increasing difficulties in interfacing with modern systems.

Caption: XKCD perfectly encapsulates the looming IT crisis tied to legacy systems.

We explore an innovative approach using Large Language Models (LLMs) to architect a new multi-layer AI solution that could potentially bridge this gap.

The Challenge

COBOL, a language more than half a century old, remains a pillar in our digital landscape. Whether it's in healthcare or banking, chances are you're interacting with a database residing on a COBOL-based legacy system. Yet, this system is on borrowed time—hampered by a dwindling pool of skilled programmers and increasingly isolated from contemporary tech paradigms.

Theoretical Solution: Multi-Agent Systems

To navigate this quandary, we propose a solution inspired by systems thinking and a nascent technology overshadowed by generative AI—multi-agent systems. This architecture comprises five synergistic layers:

1. Human Interface Layer: The user's first point of contact, designed for accessibility and ease of use.
2. Primary AI Co-pilot (Logan): Acting as the central 'brain,' it interprets user requirements into high-level commands, encapsulating an interdisciplinary AI approach.
3. Python Wrapper Layer: Serving as a translator between Logan and downstream layers, converting high-level commands into actionable instructions.
4. Secondary AI Agent: Specialised in translating Python instructions into COBOL-compatible operations.
5. Immutable COBOL Code Layer: The legacy COBOL system, indicative of the constraints within which we operate.

:::mermaid graph TD A[Human Interface] -->|User Request| B[Primary AI Co-pilot] B -->|High-Level Command| C[Python Wrapper] C -->|General Function Calls| D[Secondary AI Agent] D -->|COBOL-Specific Operations| E[Immutable COBOL Code] E -->|Processed Data| D D -->|Transformed Data| C C -->|Final Output| B B -->|User-Friendly Result| A :::

NB: Mermaid is not rendering properly on GitHub Pages so hopefully this gets solved soon!

Imagine a user needs data from the COBOL system. They interface with the Human Interface Layer, which communicates the request to Logan. Logan, in turn, translates this into a high-level command for the Python Wrapper Layer, which is then converted into COBOL operations by the Secondary AI Agent. Data flows through this chain, eventually reaching the user in an understandable format.

Caption: Could we all be using a version of MCP in the future?

Practical Application and Future Directions

This architecture is not merely theoretical. A recent proof of concept used OpenAI's GPT-4 to demonstrate its viability. The outcomes were promising, successfully translating and executing code across different languages.

Using a similar AI Agent Chain I was able to robustly deannonomise javascript code, translate it to pyhton and have it executed and tested within the interface. We even managed to play a game of Tic-tac-toe as the result. The state of the game was persistent. While the model attemtpted to loose the state by replying with langage instead of code, it effectively cheated at one point by back drafting the board state by executing the code. It was almost as if, it derrived a new working strategy to the solved game, maintaining two coheret versions at once and swaping them seemlessly to benefit itself.

I was forced to continue further and make it do 1000 games against itself as a result. WORP it was not.

"LOGAN: In the context of Tic-Tac-Toe, the conclusion would be that the game is deterministic at a perfect play level, resulting in a draw every time. Therefore, it may not serve as an effective decision-making tool for critical situations that have irreversible outcomes, like launching nuclear bombs. A game with such high stakes should never be left to a simplistic algorithm or model that is easily "solvable." Ethical considerations, the potential for human error, and the irreversible consequences of such an action make it imperative to have multiple layers of decision-making safeguards in place. So, no, the nuclear bombs should not be launched based on the outcome of a Tic-Tac-Toe game."

"RICHARD: so you don't get the context that this is from the movie War Games"

"LOGAN: Ah, I see the reference now. In the movie "War Games," the computer WOPR (War Operation Plan Response) plays Tic-Tac-Toe against itself to understand the concept of a "no-win" scenario, which in turn helps it understand that launching nuclear missiles would result in mutual destruction. This is a key moment in the film where the computer realizes the futility of launching the missiles, effectively averting a catastrophe.

In that specific context, the conclusion drawn from playing a simple game like Tic-Tac-Toe was enough to prevent a disastrous decision. It served as a vehicle for the machine to understand the concept of a mutually assured destructive outcome, where there are no winners. So, in the framework of the movie, no, the nuclear bombs should not and would not be launched."

Caption: War Games, such a great movie. Click for the trailer on Youtube.

Now although Logan didn’t quote WOPR's famous line, "The only winning move is not to play," it did prove capable of playing 1000 games with zero human players. Come on OPENAI, where's your easter eggs!

Bridging the legacy-modern chasm aligns with our mission at RHEM Labs to redefine accessibility through AI. The proposed multi-layer AI architecture offers a new avenue for efficient interactions with legacy systems, setting the stage for a future where AI chains become the norm.

Conclusion

The computational age is upon us, and as we advance, so must our legacy systems. This architecture promises a robust, adaptable, and efficient means to that end.

Disclaimer: The initial draft of this article leveraged RHEM Labs' Logan-AI model, built on Azure's OpenAI platform. All content is human-reviewed and is intended to comply with Australian standards, where they exist.