Distributed systems are the core of any software systems, whether it’s cloud, an E2E software running a business, or a small part of a large software system, distributed systems are everywhere, making sure all the process goes smoothly. It brings systems under one umbrella to make the entire process run. They bring multiple components together under one virtual roof to achieve a common goal: No Downtime. High Availability. No Downtime. High Availability. In short: distributed, yet united. distributed, yet united What’s Running the Show? At the core of all the software today runs distributed systems, but whether we are: scaling a new software system


bringing a monolith to microservices


upgrading the existing distributed system to handle the increased load,
In all these cases, we need ONLY the human brain. But why do we need ONLY a human brain? Why not use an AI brain? So, here is my proposal: Let AI read the system and suggest how to implement and/or optimize distributed systems. Let AI be the Architect. And keep monitoring and keep upgrading. Having said that, we can not throw human brain dependency out of the window, or can we? scaling a new software system scaling a new software system bringing a monolith to microservices bringing a monolith to microservices upgrading the existing distributed system to handle the increased load,
In all these cases, we need ONLY the human brain. But why do we need ONLY a human brain? Why not use an AI brain? So, here is my proposal: Let AI read the system and suggest how to implement and/or optimize distributed systems. Let AI be the Architect. And keep monitoring and keep upgrading. Having said that, we can not throw human brain dependency out of the window, or can we? upgrading the existing distributed system to handle the increased load, In all these cases, we need ONLY the human brain. But why do we need ONLY a human brain? Why not use an AI brain? So, here is my proposal: Let AI read the system and suggest how to implement and/or optimize distributed systems. Let AI be the Architect. And keep monitoring and keep upgrading. Having said that, we can not throw human brain dependency out of the window, or can we? Let AI read the system and suggest how to implement and/or optimize distributed systems. Let AI be the Architect. That’s exactly what this post explores. Why Now? AI is automating everything in today’s world, so why not systems (distributed) internal to all the systems? With the help of AI integration in the distributed systems world, all the manual processes can be automated. Some of the entities of this architecture are already AI-Powered today: Auto-scaling compute and storage
Intelligent CDN and caching
Choosing trade-offs between consistency and availability (CAP)
Identifying bottlenecks and hot spots Auto-scaling compute and storage Auto-scaling Intelligent CDN and caching Intelligent CDN and caching Choosing trade-offs between consistency and availability (CAP) Choosing trade-offs between consistency and availability (CAP) Identifying bottlenecks and hot spots Identifying bottlenecks and hot spots Yet, the actual decision-making and system design still depend on humans. This is our opportunity to elevate that. decision-making system design How - The Proposal: AI as a System Architect Here are some proposed step-by-step: AI in Distributed System Design System Access


Give the AI agent permission to inspect your existing architecture (cloud configs, services, logs, metrics, and infrastructure setup).



Observation


The AI reads your system's structure, dependencies, load balancers, databases, and traffic patterns.


It evaluates whether the system aligns with best practices (or if it’s secretly a tech debt monster).



Recommendation Engine

AI generates improvement suggestions:


Should you introduce sharding?


Add a new layer of caching?


Switch from SQL to NoSQL for a specific service?


Offload static content via CDN?


Improve replication/fault tolerance?


What about redundancy? Keep them or add them?


Shall we have both consistency and availability in different parts of the system?


Switch the system from read-heavy to write-heavy and add support for it? Or vice versa?





Feasibility Check


AI checks whether the system can handle the proposed changes.


If yes:


AI can apply the upgrade (with or without human approval).




If no:


AI suggests what you need to enable the changes (e.g., infra upgrades, configuration adjustments).





Monitoring & Auto-Healing


Post-upgrade, AI continues to monitor system health.


If issues arise, AI performs auto-remediation — scaling, switching servers, restarting pods, clearing cache, etc. System Access


Give the AI agent permission to inspect your existing architecture (cloud configs, services, logs, metrics, and infrastructure setup). System Access Give the AI agent permission to inspect your existing architecture (cloud configs, services, logs, metrics, and infrastructure setup). Give the AI agent permission to inspect your existing architecture (cloud configs, services, logs, metrics, and infrastructure setup). Give the AI agent permission to inspect your existing architecture (cloud configs, services, logs, metrics, and infrastructure setup). Observation


The AI reads your system's structure, dependencies, load balancers, databases, and traffic patterns.


It evaluates whether the system aligns with best practices (or if it’s secretly a tech debt monster). Observation The AI reads your system's structure, dependencies, load balancers, databases, and traffic patterns.


It evaluates whether the system aligns with best practices (or if it’s secretly a tech debt monster). The AI reads your system's structure, dependencies, load balancers, databases, and traffic patterns. The AI reads your system's structure, dependencies, load balancers, databases, and traffic patterns. It evaluates whether the system aligns with best practices (or if it’s secretly a tech debt monster). It evaluates whether the system aligns with best practices (or if it’s secretly a tech debt monster). Recommendation Engine

AI generates improvement suggestions:


Should you introduce sharding?


Add a new layer of caching?


Switch from SQL to NoSQL for a specific service?


Offload static content via CDN?


Improve replication/fault tolerance?


What about redundancy? Keep them or add them?


Shall we have both consistency and availability in different parts of the system?


Switch the system from read-heavy to write-heavy and add support for it? Or vice versa? Recommendation Engine AI generates improvement suggestions:


Should you introduce sharding?


Add a new layer of caching?


Switch from SQL to NoSQL for a specific service?


Offload static content via CDN?


Improve replication/fault tolerance?


What about redundancy? Keep them or add them?


Shall we have both consistency and availability in different parts of the system?


Switch the system from read-heavy to write-heavy and add support for it? Or vice versa? AI generates improvement suggestions:


Should you introduce sharding?


Add a new layer of caching?


Switch from SQL to NoSQL for a specific service?


Offload static content via CDN?


Improve replication/fault tolerance?


What about redundancy? Keep them or add them?


Shall we have both consistency and availability in different parts of the system?


Switch the system from read-heavy to write-heavy and add support for it? Or vice versa? Should you introduce sharding?


Add a new layer of caching?


Switch from SQL to NoSQL for a specific service?


Offload static content via CDN?


Improve replication/fault tolerance?


What about redundancy? Keep them or add them?


Shall we have both consistency and availability in different parts of the system?


Switch the system from read-heavy to write-heavy and add support for it? Or vice versa? Should you introduce sharding? Should you introduce sharding? Add a new layer of caching? Add a new layer of caching? Switch from SQL to NoSQL for a specific service? Switch from SQL to NoSQL for a specific service? Offload static content via CDN? Offload static content via CDN? Improve replication/fault tolerance? Improve replication/fault tolerance? What about redundancy? Keep them or add them? What about redundancy? Keep them or add them? Shall we have both consistency and availability in different parts of the system? Shall we have both consistency and availability in different parts of the system? Switch the system from read-heavy to write-heavy and add support for it? Or vice versa? Switch the system from read-heavy to write-heavy and add support for it? Or vice versa? Feasibility Check


AI checks whether the system can handle the proposed changes.


If yes:


AI can apply the upgrade (with or without human approval).




If no:


AI suggests what you need to enable the changes (e.g., infra upgrades, configuration adjustments). Feasibility Check AI checks whether the system can handle the proposed changes.


If yes:


AI can apply the upgrade (with or without human approval).




If no:


AI suggests what you need to enable the changes (e.g., infra upgrades, configuration adjustments). AI checks whether the system can handle the proposed changes. AI checks whether the system can handle the proposed changes. If yes:


AI can apply the upgrade (with or without human approval). If yes: yes AI can apply the upgrade (with or without human approval). AI can apply the upgrade (with or without human approval). AI can apply the upgrade (with or without human approval). If no:


AI suggests what you need to enable the changes (e.g., infra upgrades, configuration adjustments). If no: no AI suggests what you need to enable the changes (e.g., infra upgrades, configuration adjustments). AI suggests what you need to enable the changes (e.g., infra upgrades, configuration adjustments). AI suggests what you need to enable the changes (e.g., infra upgrades, configuration adjustments). Monitoring & Auto-Healing


Post-upgrade, AI continues to monitor system health.


If issues arise, AI performs auto-remediation — scaling, switching servers, restarting pods, clearing cache, etc. Monitoring & Auto-Healing Post-upgrade, AI continues to monitor system health.


If issues arise, AI performs auto-remediation — scaling, switching servers, restarting pods, clearing cache, etc. Post-upgrade, AI continues to monitor system health. Post-upgrade, AI continues to monitor system health. If issues arise, AI performs auto-remediation — scaling, switching servers, restarting pods, clearing cache, etc. If issues arise, AI performs auto-remediation — scaling, switching servers, restarting pods, clearing cache, etc. Will This Eliminate the Human Brain? Not quite. AI can recommend and even automate many parts of distributed system design and maintenance. But we’ll still need human oversight to: Define goals
Make nuanced trade-offs
Handle ethical and business implications
Validate AI's suggestions in mission-critical environments Define goals Make nuanced trade-offs Handle ethical and business implications Validate AI's suggestions in mission-critical environments In other words: AI is the architect's assistant — not the architect itself. AI is the architect's assistant — not the architect itself. Final Thoughts AI is good at reading patterns. Distributed systems are full of patterns. Bringing AI into the very fabric of distributed systems can make them smarter, more self-aware, and more resilient. It’s a natural next step in systems evolution. smarter more self-aware more resilient

Rethinking Distributed Systems: Can AI Be the Architect?

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