Can AI Agents Prevent Another Washington?
We all are saddened by the tragedy that took 67 beautiful lives in Washington. We, as humans, learn from such incidents and start working towards solutions to avoid them in the future. One possible solution is to use an AI agent for aviation safety. Considering all news around AI, it is quite natural to discuss, and debate it. Although our immediate reaction could be — “it is scary, how could we let AI take over the function of air traffic safety”.
Let us take a deep breath and for the sake of brainstorming, see how such an AI based solution could help in the future. This blog makes an attempt to explain the use of AI agents in air traffic control in simple terms. It is safe to assume that AI agents are not replacing human air traffic controllers in the foreseeable future. Long before that happens, they need to show up on the stage in assistive mode and prove themselves.
If AI agents are in the assistive mode, it should lessen our concerns. As we speak, a set of software services are assisting air traffic controllers. AI agents are similar to those services with very important distinctions between them. AI agents can collect unstructured data from the environment and make dynamic decisions. Whereas, software services are hardwire logic that may cover several paths but not a new one. It has to be updated to incorporate new rules.
Imagine if there would have been a Voice AI agent in the control tower, and it heard somebody say, “OMG that chopper is too high”? “Beep, beep, beep” goes another AI agent with a flashing aircraft trajectory and helicopter trajectory. In nutshell, an AI agent can draw inference based on available information that was missed in a hardwired software system. It does not need yet another integration to feed the data in structured format, rather it can consume unstructured data in natural language or CSV format.
For the illustration, this blog uses a high level architecture of a traffic control system. This is much simplified architecture. It may not have any resemblance to the real architecture. The idea is to compare conventional architecture with AI agents.
Traditional Aviation Traffic Control Architecture
The above architecture is based on microservices. Once again, this is a simplified architecture for illustration purposes.
A set of microservices receive input data from multiple sources and continuously evaluate them against a set of rules. These rules are hard-coded by developers in the microservices’ implementation. Let us assume, if developers forgot to implement the rule “Helicopter’s max height should be 200ft, else issue a high alert”. This could be a problem. Although it may not have happened in case of the Washington tragedy, it illustrates how hardwired logic works.
Let us examine how an AI agent can dynamically respond to such rules and environmental contexts.
AI Agents for Traffic Control
In the above diagram, a parallel architecture uses AI agents. We have added two additional agents, 1) a Vision Agent and 2) a Voice Agent to capture additional environmental context. Since agents can consume unstructured input, it is relatively easy to add them.
AI agents are collaborating processes. They collaborate through an orchestrator. The Rules Agent is fed with data prepared by other agents. It draws inferences by interpreting natural language rules in the context of data. These inferences may lead to actuating an alert. Let us assume that the helicopter height data was not sent by the helicopter. Instead, someone uttered words in the control room, “OMG, that chopper is too high”. These words and the rule that height has a limit, AI can conclude there is something wrong.
Using this simple illustration, we demonstrated that AI agents can receive unstructured input from the environment. AI agents using the rules described in a natural language can act as an intelligent traffic controller assistant.
AI Agent Risks and Mitigation
The biggest risk of an AI agent is what if, it infers something that is converse of the desired outcome. For example, it may push the helicopter to 300ft. Such inferences will be counter productive and even disastrous. The following are some of the mitigations.
Grounding of AI Result
This could be a cost burden, but a validation system using microservices or another set of AI agents can validate outcomes. Such a system will not be as expensive as the real traffic controller system but incorporate the same rules (implemented differently).
Testing and Validation for Several Months
It is safe to assume that these AI agents will undergo testing with simulated data and real data. Simulated data can be generated by another set of agents.
Use as an Assistant
Agents should be used in assistive mode as helpers to a real human.
Interesting Use Case of AI Agents
Let us say, you are not ready to deploy AI agents in a control tower. Still, there is a very interesting use of them. They can work offline on the same set of data and produce signals. These signals can be compared with the signals generated by the actual system. It will bubble up the signals that are missed by the actual network traffic control system. This way, you can find the gap in the micro services implementation and use this information to improve them.
This is the case where instead of providing training data to train a model, you are using AI output to make a conventional system more intelligent.
Conclusion
AI is the future. This article describes how you can incorporate AI in a stepwise manner in a scenario. Let us hope that one day, AI will avoid another Washington.
