I think the 'democratization' phase mentioned here is going to happen faster than people think, thanks to tools like monobot.ai making AI Chat and Voice agent deployment significantly easier for non-technical teams.
LLMs are excellent stochastic generators, but they’re not full decision systems. They lack persistent objectives, constraint checks, causal models, and verification layers. That’s why the next frontier isn’t a bigger model—it’s Modular Intelligence (MI).
MI treats the LLM as a computational primitive inside a larger control architecture:
• Planner module: decomposes goals into tractable subproblems
This is an incredibly thorogh collection of resources for anyone building agentic systems. The progression from video masterclasses to hands-on repos to research papers makes perfct sense as a learning path. What strikes me most is how quickly agents are moving from experimental to production-ready, especially with frameworks like MCP making it easier to build reliable multi-agent workflows.
Hi Ruben, thanks for sharing this roadmap. I put this exact roadmap together a couple of months ago for the community and I would really appreciate a mention.
thanks, Ruben. No harm done. Unfortunately the creator of that GDoc lifted the resources of my post and shared it on LinkedIn. They have since added my name to the LinkedIn post in which they shared the documents link.
Solid roadmap, thanks!
I think the 'democratization' phase mentioned here is going to happen faster than people think, thanks to tools like monobot.ai making AI Chat and Voice agent deployment significantly easier for non-technical teams.
LLMs are excellent stochastic generators, but they’re not full decision systems. They lack persistent objectives, constraint checks, causal models, and verification layers. That’s why the next frontier isn’t a bigger model—it’s Modular Intelligence (MI).
MI treats the LLM as a computational primitive inside a larger control architecture:
• Planner module: decomposes goals into tractable subproblems
• Constraint & policy module: enforces legal, safety, and domain rules
• Simulator module: runs counterfactuals and causal checks
• Verifier module: validates outputs, catches drift, resolves contradictions
• Adversarial module: stress-tests assumptions and searches for failure modes
• Memory/state module: keeps persistent context and long-horizon consistency
The LLM slots into these pipelines as a flexible reasoning tool, not the governance layer.
This architecture fixes the known weaknesses of raw LLMs:
• unbounded reasoning becomes bounded, rule-governed computation,
• hallucinations are caught at the verifier layer,
• plans become auditable and reproducible,
• domain knowledge is encoded in modules instead of prompts,
• and system behaviour remains stable even as underlying models change.
In short:
LLMs give you undirected cognitive horsepower.
Modular Intelligence turns that horsepower into reliable, steerable, fault-tolerant intelligence.
It’s the difference between a powerful engine and a complete vehicle with steering, brakes, instrumentation, and safety systems.
This is an incredibly thorogh collection of resources for anyone building agentic systems. The progression from video masterclasses to hands-on repos to research papers makes perfct sense as a learning path. What strikes me most is how quickly agents are moving from experimental to production-ready, especially with frameworks like MCP making it easier to build reliable multi-agent workflows.
Hi Ruben, thanks for sharing this roadmap. I put this exact roadmap together a couple of months ago for the community and I would really appreciate a mention.
For your reference, here's my original post: https://www.linkedin.com/posts/sairam-sundaresan_over-77999-tech-jobs-lost-to-ai-already-activity-7366428033915756545-DVSM
Thanks a lot for sharing! I've chosen the best resources I saw on a similar GDoc (there was no author mentioned).
Great stuff 👏
thanks, Ruben. No harm done. Unfortunately the creator of that GDoc lifted the resources of my post and shared it on LinkedIn. They have since added my name to the LinkedIn post in which they shared the documents link.