Aseem Rastogi.
Software & AI Architect · Co-Founder & CTO, Agentcord.ai
Ex-Architect, iXiGo · Ex-Staff Engineer, Synaptic · Ex-Senior Computer Scientist, Belzabar · B.Tech CSE, NIT Hamirpur (Gold Medalist)
With 12+ years of experience shipping production software, Aseem has architected and led the systems behind some of India's highest-traffic consumer platforms, and over the last 3 years has brought that same depth to building production-grade agentic AI for real businesses.
Today, Aseem is building Agentcord.ai on a deliberate two-layer agentic architecture: a deterministic orchestration plane in LangGraph that handles goal setting, context assembly, and agent routing, paired with a Temporal-based autonomous execution engine that runs long-horizon agent workflows surviving failures, retries, and multi-day cycles. Every agent executes inside a gVisor-sandboxed runtime, communicates with the outside world over an MCP-mediated tool plane, composes its behaviour from a custom Skills layer, and is observed continuously by an Agent Evals harness, with the entire platform deployed on EKS.
Before Agentcord, Aseem was the Architect who led the entire Trains & Bus backend at iXiGo for five years, owning its team and its systems end to end.
From that period came CrowdSource Running Status, the flywheel that drives organic customer acquisition at iXiGo to this day, built directly with Prashant Ghidiyal, then VP Tech at iXiGo and today the GenAI and Cybersecurity head at Delhivery (and ex-CEO of Devtron Labs). The system ingests tens of millions of GPS and cell-tower events daily through a real-time streaming pipeline on Apache Spark fused with carrier APIs, and powers a real-time delay-prediction model built on ARIMA that set a new bar for running-status accuracy across the industry.
In his tenure he led the rearchitecture of the entire trains backend, graduating it from a Monolithic codebase to Microservice Architecture, and continuously evolved it into a service mesh architecture powered by Kubernetes and Istio, which added capabilities like dynamic, service-aware routing that treated canary and blue-green rollouts as first-class, replaced the previous Kibana-centric logging with an end-to-end NewRelic + Grafana + Loki observability stack, hardened failure isolation through a significantly improved Hystrix-based compartmentalisation layer, and introduced a Redis + ScyllaDB + Aerospike operational data layer tuned for the new latency and throughput profile.
Throughout his tenure he worked directly under iXiGo's CTO Rajnish Kumar (now Co-CEO of the company); for other projects he collaborated with industry leaders like Ram Singla, today CEO of Temple at Eternal (the parent company of Zomato).
Out of that same rearchitecture came an availability-prediction model that forecasts train-seat availability across routes and classes, shipping today inside the iXiGo train-search experience. From the same years came iXiGo's Travel Graph, a multi-modal A2B search graph on JanusGraph, ScyllaDB, and ElasticSearch that plans a single journey across bus, train, and flight in one query, and a real-time CDC pipeline on Kafka Connect and Debezium that propagated trip and transaction state across the platform.
He then took the entire Core Data Engineering function at Synaptic under his leadership, a 13-person team working directly with Anurag (CTO of the company), where he architected a workflow orchestrator on EKS, Apache Pulsar, JanusGraph, Apache Spark, and Apache Hudi, with Dask handling out-of-core parallel compute, that today runs 10,000+ concurrent pipelines over 10+ TB of data daily, and built out Synaptic's semantic-search and vector-embedding stack on ElasticSearch and ClickhouseDB alongside the Research team.
He recently served as architectural advisor on Squizify (Australia)'s full-stack modernisation and GenAI adoption, helping the world's first AI-powered Food Security Compliance platform evolve its core compliance workflow from a reactive system into a proactive, anticipatory agentic one. Separately, he continues to provide ongoing technical assistance to the Government of Himachal Pradesh's Hydrology Department, where his real-time sensor pipelines and flood-prediction models now run state-wide.
One thread runs through all of it: production depth, shipped at scale, alongside the engineering leaders who set that bar. This is not a course taught from tutorials. It is taught by the architect who built the systems.
Glad to be part of this cohort. What stands out is the practical depth, not just tools, but how AI, system design, and agentic patterns come together for real-world engineering. Looking forward to learning more.
What I appreciate most is the depth of learning. Instead of just covering the “what,” the cohort dives into the “how” and “why” behind AI concepts. Great experience so far!
Aseem's masterclass finally made AI click for me beyond just writing prompts. He goes deep into how the models and agents actually work under the hood, the attention math, the agent loops, the evals, exactly the kind of depth you need as an engineer who wants to build with AI, not just use it. Genuinely one of the most useful technical programs I've done in years.
Joining this cohort was one of the best decisions I made for my AI learning journey. Before this, I was unsure where to start and overwhelmed by the noise around AI. Aseem's sessions gave me clarity, strong fundamentals, and the confidence to build my own agents. The focus on basic principles and real-world systems makes all the difference.
I learned a lot about the internal workings of AI, which is helping me use AI far more effectively for technical and complex problem-solving tasks.
The pace is intense and the depth is real. We built things from scratch instead of gluing libraries together, and that completely changes how you think about the stack. Easily the best technical cohort I've taken.
I would highly recommend this cohort to anyone who wants to understand Agentic AI beyond the hype and surface-level tutorials. What makes this program stand out is the way it combines fundamentals, system design, and real-world implementation thinking. The cohort does not just focus on tools or quick demos. It helps you understand how AI systems are actually designed, how LLMs and agents fit into modern product architectures, and how to reason about them as an engineer. If you want depth instead of buzzwords, this is the cohort to join.
Genuinely one of the best learning experiences I've had as an engineer. Aseem takes dense AI and systems topics and turns them into something you can actually build with, every session moves you from theory to working code. It's the rare cohort that respects your time and assumes you want real depth. Highly recommend it to anyone serious about going beyond the surface.
As a staff engineer, what I value most is depth and first-principles thinking, and this cohort delivers both. Aseem connects the math, the systems, and the production reality in a way I haven't seen in any other program. It's rare to find teaching that is this rigorous and this practical at the same time. I came in to fill gaps and left with a genuinely stronger mental model of the whole stack.
I've done plenty of online courses that stay at the surface. This one goes all the way down, tokenization, attention, agent loops, evals, and then back up to production. I finally feel like I understand AI instead of just using it.
As someone coming from a backend and system-design background, this cohort has helped me connect traditional engineering principles with modern AI systems. Every session leaves me with a long list of things to explore and apply. Great learning experience so far.
I'm attending this weekend cohort on AI agents, and now I finally understand how AI and agents actually work. Earlier, AI was just magic to me, now I understand the machinery behind it. Thanks Aseem for these sessions.
Coming from a distributed-systems background, I expected the AI parts to feel hand-wavy. They didn't. Every concept is grounded in how you'd actually design, ship, and operate it, latency, failure modes, evals, the works. This is the most engineering-honest AI course I've come across.
Glad to be part of this cohort. What stands out is the practical depth, not just tools, but how AI, system design, and agentic patterns come together for real-world engineering. Looking forward to learning more.
What I appreciate most is the depth of learning. Instead of just covering the “what,” the cohort dives into the “how” and “why” behind AI concepts. Great experience so far!
Aseem's masterclass finally made AI click for me beyond just writing prompts. He goes deep into how the models and agents actually work under the hood, the attention math, the agent loops, the evals, exactly the kind of depth you need as an engineer who wants to build with AI, not just use it. Genuinely one of the most useful technical programs I've done in years.
Joining this cohort was one of the best decisions I made for my AI learning journey. Before this, I was unsure where to start and overwhelmed by the noise around AI. Aseem's sessions gave me clarity, strong fundamentals, and the confidence to build my own agents. The focus on basic principles and real-world systems makes all the difference.
I learned a lot about the internal workings of AI, which is helping me use AI far more effectively for technical and complex problem-solving tasks.
The pace is intense and the depth is real. We built things from scratch instead of gluing libraries together, and that completely changes how you think about the stack. Easily the best technical cohort I've taken.
I would highly recommend this cohort to anyone who wants to understand Agentic AI beyond the hype and surface-level tutorials. What makes this program stand out is the way it combines fundamentals, system design, and real-world implementation thinking. The cohort does not just focus on tools or quick demos. It helps you understand how AI systems are actually designed, how LLMs and agents fit into modern product architectures, and how to reason about them as an engineer. If you want depth instead of buzzwords, this is the cohort to join.
Genuinely one of the best learning experiences I've had as an engineer. Aseem takes dense AI and systems topics and turns them into something you can actually build with, every session moves you from theory to working code. It's the rare cohort that respects your time and assumes you want real depth. Highly recommend it to anyone serious about going beyond the surface.
As a staff engineer, what I value most is depth and first-principles thinking, and this cohort delivers both. Aseem connects the math, the systems, and the production reality in a way I haven't seen in any other program. It's rare to find teaching that is this rigorous and this practical at the same time. I came in to fill gaps and left with a genuinely stronger mental model of the whole stack.
I've done plenty of online courses that stay at the surface. This one goes all the way down, tokenization, attention, agent loops, evals, and then back up to production. I finally feel like I understand AI instead of just using it.
As someone coming from a backend and system-design background, this cohort has helped me connect traditional engineering principles with modern AI systems. Every session leaves me with a long list of things to explore and apply. Great learning experience so far.
I'm attending this weekend cohort on AI agents, and now I finally understand how AI and agents actually work. Earlier, AI was just magic to me, now I understand the machinery behind it. Thanks Aseem for these sessions.
Coming from a distributed-systems background, I expected the AI parts to feel hand-wavy. They didn't. Every concept is grounded in how you'd actually design, ship, and operate it, latency, failure modes, evals, the works. This is the most engineering-honest AI course I've come across.
Pick a cohort.
Every Hubbleflow cohort is taught live by Aseem. Choose the track you want to go deep on.
Agentic AI Mastery
From Software Engineer to AI Architect.
You already use AI to write software. This course teaches you to write software that is AI.
Reinforcement Learning
From Bellman equations to GRPO.
Every agentic capability we see in today's frontier models, the long-horizon thinking and the step-by-step decomposition of a task, comes from reinforcement learning. And RL is the core of modern robotics too.
System Design
From back-of-the-envelope to global scale.
System design is what separates engineers who write code from engineers who architect what the world runs on. It decides whether software scales or breaks.