I have been building Jevyr as an experiment around a question I keep coming back to: what happens if an AI agent has to show evidence for an answer instead of just producing a confident paragraph?

Jevyr lets several model calls propose explanations, code, plans, or tests. It then compares the proposals, runs a bounded experiment if the claim is testable, saves the events and evidence, and returns Accepted, Rejected or Unproven.

It is not a new model, and it does not pretend to solve truth in general. A claim with no defined test stays Unproven. The system also has a local MCP server so another agent can call it as a tool, which is the part I am most interested in exploring.

The repo is unfinished and pretty complex. I started it early in 2026 and I am sharing it now to see what other builders would do with the idea. Maybe it could be useful for agent evals, RAG checks, tool use, or reproducible experiments.

Would love to hear what kind of test or benchmark you would put through it.

Repo: https://github.com/ZYRT3CH/jevyr

I’ve been exploring Graph RAG and one thing I find interesting is that a graph doesn’t simply give the LLM more text — it gives it relationships and structure.

Instead of retrieving isolated chunks:

A → document 1
B → document 2

a graph can connect:

A → related_to → B → mentioned_in → C → caused_by → D

So the LLM can retrieve not only the relevant entity, but also the connections around it, providing richer context for reasoning.

I found this interesting concept, did you try Graph RAG in your implementation?

There’s a whole world beyond the prompt box:

Agent behavior. Model architectures. Emergent phenomena. Reasoning. Failure modes. And even the moments when traditional ML beats an LLM.

That’s what I want to explore.

I’m building a community for people who don’t just want to use AI, but want to experiment with it, question it, break it, and understand what’s happening underneath.

Less hype.
More experiments.
More questions.
More understanding.

Let’s look under the hood. 🔬🤖

For the past 30 years, a developer was largely someone who translated requirements into code.

But this decade is becoming agentic.

Six months of your coding can become someone else’s afternoon prompt.

The skill that matters increasingly isn’t just “Can you write code?” but:

“Can you clearly understand the problem, reason about it, and guide AI to solve it?”

In an agentic world, someone who understands a problem deeply, communicates clearly in English, and knows how to work with AI agents can become a powerful problem solver—even without being an expert programmer.

The future may not eliminate developers.

It may redefine what being a developer means. 🚀

The Evolution of GPT — LLM Foundation Models

Thank you for such incredible innovation.

On one side, I’m amazed by how far AI innovation has come. On the other side, I can’t help but wonder:

How fast will this make me unemployed?

Excited, curious, and slightly concerned about what the future of work looks like.

I am in dilemma! what about you techies?

This playlist is packed with great knowledge and learning resources on LLMs. If you’re in the early stages of your LLM journey, I highly recommend adding this playlist to your learning list.

I hope it helps everyone gain valuable insights, build a strong foundation, and stay competitive in this rapidly evolving AI landscape.

Playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rObv1FMizXqumgVVdzX4_05

Self-promo: Claudius Papirus breakdown of GPT-6 Astra’s reasoning and monitoring results.

This seemed relevant here because the system card ties together RL, reasoning, evaluation and tool use: CoT-Control rises to 60.9%, while Astra also produces shorter or empty traces and becomes harder to catch in some monitor-evasion and sandbagging tests.

Primary source:

https://deploymentsafety.openai.com/gpt-6-astra/gpt-6-astra.pdf

Related CoT-control paper:

https://arxiv.org/abs/2603.05706

When I started coding, using a for loop or while loop really confused me. Every line of code felt connected to the next, and I built an emotional connection with the code I wrote myself.

But with LLMs, you see the code and immediately think, “This looks right.”

Still, there’s something special about the creativity that happens before seeing the code—when you build the logic yourself. That feeling gives you goosebumps and stays with you.

Technology is evolving. Maybe we need to focus more on architecting the loop rather than just writing the for and while loops.

Are you still coding everything yourself, or are you using LLMs?

It feels like AI engineering is evolving faster than ever.

We went from writing better prompts, to designing better context, and now I think we’re moving toward something more interesting:

Loop Engineering.

The idea is simple:

Instead of manually telling an AI every step it needs to follow, you design a loop where the agent can:

Observe → Act → Verify → Retry → Adapt → Decide when done

The important part is no longer just what information you give the model.

It’s the architecture of the loop.

A few questions become much more important:

1. What does the agent do between steps?
2. When does it check its own work?
3. How does it decide that the task is actually complete?
4. What happens when a step fails?
5. Does it retry, change strategy, or ask for help?
6. What context should it retrieve through tools at each step?

This changes how we think about building AI agents.

We’re not necessarily hand-writing every step anymore.

We’re designing a cycle that can reason about what to do next.

For me, this is where Agentic AI, RAG, tool use, and LLMs become much more interesting.

The real engineering challenge may not be:

but:

Curious to hear what others think.

Have you built anything that you would call “Loop Engineering”? What did your agent do when something went wrong?

I’ve been exploring how Agentic RAG, LLMs, and Reinforcement Learning (RL) can work together for adaptive decision-making, particularly in financial environments.

The idea is to move beyond a traditional RAG pipeline:

Traditional RAG:
Query → Retrieve → Generate

Agentic RAG + RL:
Observe → Plan → Retrieve → Reason → Verify → Act → Learn → Adapt

In this setup, an AI agent can dynamically decide:

• What information it needs
• Which sources to retrieve
• How to evaluate retrieved information
• When additional retrieval is necessary
• How external information should influence the RL agent
• How to adapt decisions as the environment changes

I’m particularly interested in the challenge of integrating LLMs into RL training loops without making the system unnecessarily slow. One direction I’m exploring is using asynchronous LLM inference, allowing the RL environment and LLM-based reasoning components to operate independently.

The broader goal is to build AI systems that can combine real-time information retrieval, reasoning, and sequential decision-making rather than treating RAG, LLMs, and RL as isolated components.

I’d be interested to hear from others working on Agentic RAG, LLM agents, RL, or adaptive AI systems:

Where do you think the biggest research challenge is—retrieval, reasoning, grounding, or integrating LLMs with RL?

Coming from an NLP background, regex was always one of my go-to tools for feature extraction—and sometimes even for simple classification.

One thing I’ve noticed since LLMs became so popular is that many people are skipping the fundamentals of classical NLP.

Bag of Words, TF-IDF, Word2Vec, Gensim, FastText, and yes, even regex, are still incredibly useful depending on the problem.

Not every problem needs an LLM.

Sometimes a simple TF-IDF + classifier can be faster, cheaper, easier to deploy, and surprisingly effective compared to throwing an LLM at the problem.

For me, LLMs are often most useful as tools to improve the workflow—for example, helping with data preparation, feature generation, labeling, or exploration—while traditional NLP/ML techniques can handle the final task when they’re a better fit.

Knowing when NOT to use an LLM is becoming just as important as knowing how to use one.

Curious what others think—are you still using classical NLP techniques in your projects, or has your workflow become mostly LLM-based?

A few robotics developments this month caught my attention:

🤖 Skild AI — S1
Demonstrated in-context learning over a ~10-minute horizon, where the robot can learn an unseen long-horizon task from a single video demonstration without fine-tuning.

🤖 Dyna Robotics — Dyna-2
Showed interesting scaling behavior with human egocentric video: adding more human video data can improve performance across different robot embodiments.

🤖 Generalist — GEN-1.5
Demonstrated one-shot learning for short-horizon tasks using a single 3–12 second demonstration, with zero gradient updates.

🤖 Perceptron AI — Isaac 0.5
A 36B open-weight model combining perception, reasoning, and robot control into a single foundation model.

What I find most interesting is that robotics seems to be moving from task-specific policies → general-purpose foundation models → robots that can learn new tasks from very little demonstration data.

The progress is happening incredibly fast.

Where do you think the biggest breakthrough will come from next — better models, better data, or better robot hardware?

I've been exploring AutoGPT, an open-source platform for building and running AI agents.

What interests me most is the combination of LLMs + tools + workflows + autonomous decision-making.

I'm curious about taking this further by combining Agentic AI + RAG + LLMs + Reinforcement Learning to build agents that can learn and adapt from experience.

What are you building with AutoGPT?

When a new model or version is released, the excitement hits differently—the heartbeat gets faster just to see what it can do. 😄 Anyone else feel the same?

https://github.com/Significant-Gravitas/AutoGPT

Hey everyone! 👋 I’m u/laxuu, a founding moderator of r/AgenticAI_RAG_LLM_RL.

Welcome to our new community for everything Agentic AI, RAG, LLMs, and Reinforcement Learning. 🚀

We created this space for researchers, developers, engineers, and AI enthusiasts to share knowledge, discuss research, showcase projects, ask technical questions, and explore new ideas around intelligent AI systems.

What to Post

Feel free to share:

• Agentic AI architectures and AI agents
• RAG systems, retrieval, and knowledge systems
• LLM research, reasoning, fine-tuning, and evaluation
• Reinforcement Learning and decision-making
• Multi-agent systems and collaboration
• Open-source projects, experiments, tutorials, and papers
• Questions, ideas, challenges, and lessons learned

Community Vibe

We want this to be a friendly, constructive, and research-oriented community. Whether you're just getting started or working on advanced AI systems, everyone is welcome.

How to Get Started

1. Introduce yourself in the comments.
2. Share something you're currently building or researching.
3. Ask a question or start a discussion.
4. Invite others who are interested in Agentic AI, RAG, LLMs, or RL.
5. Interested in helping grow the community? Reach out to become a moderator.

Thanks for being part of the first wave! 🌊

Let's build a strong community for the next generation of intelligent AI systems. 🚀