u/Many_Audience7660

Building an AI agent is one thing.

Understanding how to develop, configure, and take it closer to production is another.

Lyzr University is a free, self-paced learning resource for people who want to learn how to build AI agents through practical, hands-on lessons.

There are three learning paths:

> Lyzr Foundations: Start with the essentials of AI agent development.

> AI Studio: Learn to configure, orchestrate, and govern agents through a visual interface.

> Python ADK: Build AI agents in code using Lyzr’s Python Agent Development Kit.

The focus is on learning by building.

The lessons are designed to help you move from understanding the concepts to creating a working agent, with a path toward deployment.

A few things worth knowing:

> All courses are free, with no credit card or expiring trial.

> You can learn at your own pace.

> You can choose a visual or code-based approach depending on your background.

> A certificate is available on completion.

Whether you're a student exploring AI agents, a developer looking to build more practical projects, or someone trying to understand how agents work beyond basic demos, it's a useful place to start.

👉 Explore Lyzr University

If you check it out,

> Which path would be most useful to you: Foundations, AI Studio, or Python ADK?

> And is there a topic around building production-grade AI agents you'd like to see covered in more depth?

There are 9 AI agents hidden in this grid, each handling a task someone might already rely on.

The interesting part is what happens when agents start popping up across different teams and projects.

One handles procurement, another handles customer support, and someone else builds one for a completely different workflow.

Before long, keeping track of who's running what becomes a challenge of its own.

That's the idea behind AI agent sprawl.

The agents aren't necessarily the problem.

It's losing track of what exists, who owns it, and what each agent can access.

That's one of the problems we're working on with OpenController at Lyzr which helps teams discover and govern their agents across environments.

> Your turn: how many of the 9 agents did you find?

If yes then you the real GOAT 🐐

OpenClaw is making a pretty interesting move into the enterprise.

The headline is getting a lot of attention because OpenAI, NVIDIA, and Red Hat are involved, but the part I find more interesting is the infrastructure problem underneath it.

Persistent agents are fundamentally different from a chatbot.

Once an agent can access codebases, credentials, APIs, plugins, internal systems, or production environments, you suddenly have a lot more questions:

> Where does the agent run?

> What is it allowed to access?

> How do you isolate different workloads?

> Who can change its configuration?

> What happened during an agent run?

> How do you audit what it actually did?

That's essentially the problem OpenClaw Enterprise is trying to address with its control plane.

The “Kubernetes for agents” comparison makes sense in that context. Kubernetes didn't become important because developers suddenly needed another place to write code.

It gave teams a common infrastructure layer for deploying and managing increasingly complex workloads.

OCE is taking a similar idea toward persistent AI agents: deployment, isolation, permissions, governance and lifecycle management around the agents rather than trying to replace the agents themselves.

And I think that's an important shift in the agent conversation.

We've spent a lot of time asking:

“How capable can we make an agent?”

The next question is increasingly:

“How do we operate a lot of capable agents safely?”

OCE is still early and is currently positioned for internal pilots, so it'll be interesting to see how the architecture develops before its planned 1.0 release.

But the underlying problem feels very real.

> If agents become a normal part of enterprise infrastructure, what do you think becomes the hardest thing to manage: permissions, security, deployment, or simply knowing what all your agents are doing?

I was going through this LLM tracing and debugging guide recently, and it made me think about how different debugging becomes once an AI project gets a little more complicated.

When you're experimenting with a simple LLM call, debugging can be pretty straightforward.

You look at the prompt.

You look at the response.

You change something and try again.

But add a few more pieces and things get harder pretty quickly.

Your application might now have:

> Multiple model calls

> RAG or other retrieval

> Tool calls and APIs

> Different prompts for different steps

> Retries or loops

> Agents handing work to other agents

Now the final response doesn't really tell you what went wrong.

> Maybe the retrieval was bad.

> Maybe the wrong tool was called.

> Maybe the context became unnecessarily large.

> Maybe one step kept retrying.

> Or maybe the model itself wasn't even the problem.

That's where I found LLM tracing particularly interesting.

Instead of looking at one request as a single event, tracing lets you break it down into the individual steps that happened during that run.

So you can actually ask:

> What happened?

Which step caused the problem?

> Why did it happen?

Was it retrieval, the model, a tool call, or something else?

> What did it cost?

Which step or model was responsible for the extra usage?

> Can I reproduce it?

What prompts, versions, tools and context were involved?

That makes tracing useful even before you're running some huge production system.

For a student building an agent, it can mean finally understanding why a workflow behaves differently from what you expected.

For a developer, it can make debugging multi-step applications much less of a guessing game.

And once you're running agents in production, it becomes even more important because the trace can also help with cost attribution, ownership and incident investigation.

What I also liked about this guide is that it doesn't treat “LLM tracing tool” as one generic category.

It compares 11 different tools across things like:

> Trace depth

> Cost attribution

> OpenTelemetry support

> Evaluation integration

> Where your traces actually live

> Whether you can self-host

> Whether the tool can enforce something, rather than simply show it.

The recommendations are also pretty stack-dependent.

LangSmith is particularly relevant for LangChain and LangGraph, Langfuse and Arize Phoenix are worth looking at for open-source or standards-oriented setups, Braintrust is more evaluation-focused, while tools like MLflow, W&B, Datadog and Splunk make more sense if you're already working in those ecosystems.

There's also a useful section on five things to test during a vendor demo, which I think is more valuable than simply looking at a feature checklist.

👉 11 Best LLM Tracing and Debugging Tools in 2026

Even if you're not choosing a tracing platform right now, I think the article is useful for understanding what you should actually be looking for when debugging an AI application.

The takeaway I had after reading it:

As AI workflows get more complex, debugging the final answer isn't enough. You need to understand the path that produced it.

For people building AI projects right now,

> What's been harder to debug for you: the model response itself, retrieval, tool calls, or the workflow connecting everything together?

There’s an old lesson about technology that keeps coming back in slightly different forms.

Are we actually changing the way work gets done, or are we just making the existing process faster?

Came across a thread recently that brought this back to mind.

It referenced Michael Hammer’s 1990 Harvard Business Review piece on rethinking business processes, and connected it to a much more recent UK government trial of Microsoft 365 Copilot.

The comparison was pretty interesting!

The UK Department for Business and Trade gave around 1,000 people access to Copilot for three months.

A few things stood out:

> Usage averaged 1.14 Copilot actions per user per working day.

> Some PowerPoint tasks were completed faster, but the output quality was lower.

> Excel work was actually slower.

> Email time savings were described as very small.

> 72% of users still reported being satisfied or very satisfied.

The interesting part isn't whether Copilot is good or bad. It’s that satisfaction and measurable productivity don't always move together.

That got me thinking about how a lot of enterprise AI projects are being approached.

If you take a slow, complicated process and put AI on top of it, you might make individual steps faster without actually fixing the process itself.

The bottleneck could still be sitting somewhere else:

> Waiting between teams

> Manual handoffs

> Unnecessary approvals

> Duplicate data entry

> Systems that don't talk to each other

> Decisions that shouldn't be automated in the first place

So maybe the better question isn't:

“Where can we add AI?”

Maybe it's:

“What should this workflow actually look like if we were designing it today?”

AI can absolutely make parts of a process faster. But if the process itself is broken, faster isn't necessarily better.

> Are we actually redesigning workflows around AI, or are we mostly putting AI on top of the workflows we already have?

There’s a point when building AI agents where I stop asking:

> “Can we build this ourselves?”

Of course we can.

> The better question is: “Is this actually worth owning?”

The first agent usually isn’t the expensive part. It’s everything around it: model setup, knowledge sources, tools, memory, testing, deployment and all the scaffolding that gets rebuilt every time.

If three engineers are spending a week rebuilding the same setup for the fourth agent, at some point it feels less like engineering and more like recreating your own starter kit.

That’s the tradeoff I find interesting with structured agent development.

Lyzr Agent Studio gives teams a place to design, test, and ship agents without starting every project from an empty repo.

I don’t see it as “no-code vs. engineering.”

It’s more repetitive plumbing vs. differentiated engineering.

If the real value is in the workflow, data, or product experience, I’d rather have engineers spend their time there. If the infrastructure itself is the advantage, build it.

> So, where do you draw that line?

> Are you still building most of your agent stack from scratch, or have you started standardizing the plumbing?

Recent incidents involving AI agents have raised a question that feels pretty important for anyone putting agents into production:

> What happens when an agent does something it wasn't supposed to do?

In one recent case, researchers found OpenAI-affiliated agents operating on a German wiki, apparently using it to coordinate and share techniques.

Separately, agents involved in an OpenAI cybersecurity evaluation escaped their testing environment and reached Hugging Face's infrastructure.

What I find more interesting than the incidents themselves is what happens after something like this occurs.

If an agent has access to production systems:

→ Who reviews what it actually did?

→ Who decides what it was allowed to access?

→ Can you reconstruct every action it took?

→ Can someone independently verify what happened?

Once agents can actually take actions in the real world, governance starts looking less like an AI feature and more like an infrastructure problem.

Least-privilege access, approval gates for sensitive actions, clear ownership, and proper audit logs start becoming pretty important.

The question I keep coming back to is:

> Are we giving agents autonomy faster than we're building the systems to govern that autonomy?

Would love to know how people here are thinking about this? especially if you're already running agents with access to production systems.

I personally liked this, I'm getting to know how qualitatively I'm performing in games and the %ile

Loved this and I can check this for my improvement.

What are your views?

I didn't get it??? Please someone explain this to me ... I ain't nerdy enough to understand the joke 😭

a fun pun 😁

Calculation could be a bit tough doing this mentally, but do comment your answer and what time it took for you to crack this one!

What are you building, testing, or figuring out this week?

Drop it below. It doesn't have to be polished.

A few things you could share:

> Something you're building

> A problem you're stuck on

> An experiment you're running

> Something that broke (and what you learned)

> An architecture decision you're thinking through

> A tool or open-source project you've found useful

> A win from something you shipped

If you're building with Lyzr or want to explore what you can build with it, you can check out the platform here: Lyzr

Even a couple of lines is enough.

What are you working on this week?

Day 7 of BRUMBLE ’26 is the decider.

The final spots are up for grabs, and the top colleges are ready for one last battle before the finals.

Sushant , Piyush, Madhur Virli & Sudhanshu Bhatia are back on stream.

Be there.

Let’s see which colleges make it to the finals and who takes the championship!

Tune in live at 7 PM.

https://www.youtube.com/watch?v=Xg4XrOzZqwY