Introduction
Agentic AI isn’t a more advanced chatbot anymore. It’s a structured approach to designing digital workflows that combine reasoning, memory, automation, and integrations. And founders are betting on it because it delivers results.
In this article, we unpack the key takeaways from a recent tech talk hosted by Ralabs and Katara AI. If you’re serious about applying AI beyond the MVP demo, here are the insights that will definitely come in handy:
What makes AI agents different
AI agents are having a breakout moment, but very few of them are built to last. Most tools in the market are thin layers on top of LLMs like OpenAI, with no infrastructure, no memory, and no path to real ROI.
What sets agentic systems apart is their ability to:
- Persist context over time – not just within a chat session
- Take action across platforms: codebases, CRMs, docs, and cloud
- Learn from past answers and improve autonomously
- Blend reasoning (LLMs) with structured execution (logic trees, APIs, workflows)
Think of them as teammates and not just tools. They’re designed to do what junior employees would do: search, reason, decide, and act.
“Anyone can spin up a chatbot. The hard part is getting the right answer, at the right time, with the right context.
James Hotson, Co-Founder of Katara
The Discord use case
If you’re wondering whether any of this actually works at scale, take a look at Discord. One of Katara’s deployments answered 3,800 messages in a single day with a 90%+ accuracy rate and source citation baked in.
It was an AI-powered support layer that:
Ingested tribal knowledge from live conversations
Indexed thousands of documents across tech, product, and marketing
Prioritized responses based on user profiles and tone
Improved with every expert interaction – learning from human replies
The system was so effective, companies started using it as a blueprint for internal agents too – in Slack, email, and knowledge bases. Community managers who once capped out at 100 replies per day were now supervising systems doing 40x the output.
Why most AI fails without good data
One of the biggest challenges in scaling AI agents is data prep. If your knowledge base is a mess, the agent will reflect that. That’s why successful teams treat data not as a byproduct, but as the product.
At Katara, the approach begins with a data audit: what documentation exists, how it’s structured, and where the gaps are. Then they apply frameworks, which break down content into four core types: tutorials, how-to guides, references, and explanations.
This also helps surface what James calls “hidden spots”: areas of the business where users are asking the same questions again and again, but there’s no good documentation to support them. By analyzing unanswered queries, these agents are responding and at the same time they’re telling you what’s missing.
Why AI still needs a human touch
Despite rapid progress in AI performance, human involvement remains essential. The most effective agentic systems are those designed with human oversight not as a fallback, but as a core element of the process.
In one example shared during the talk, Katara implemented a workflow where each AI-generated document was presented alongside a clearly marked comparison with the original version. This allowed the user to review what had changed, understand why, and either approve or adjust the output with a single click. Although this may not be the most time-efficient approach from a purely technical standpoint, it significantly increased trust in the system and accelerated long-term adoption.
Security is not a feature. It’s a prerequisite
One of the most overlooked, yet critical, components of any AI deployment is data security. Many businesses still interact with public LLMs in ways that leak valuable information without realizing it. For example, uploading internal files to a shared model like ChatGPT can unwittingly contribute to external training data, which creates long-term exposure.
To counter this, Katara takes a layered approach. For clients with sensitive data, they deploy isolated model instances running locally or in controlled environments. These setups can anonymize inputs, store embeddings securely in vector databases, and apply strict permission levels that mirror internal roles.
As Roman from Ralabs shared during the talk, their team is developing an internal Slack-based FAQ agent to handle common employee questions like vacation balance, public holidays, and onboarding updates. The agent pulls data from Google Drive and HR systems, but access is tightly scoped – each employee only sees what’s relevant to their role. It’s a practical example of how agentic AI can support internal ops without compromising data security.
Individual prompts won’t transform a company
While personal productivity tools like ChatGPT have sparked enthusiasm, their impact remains relatively shallow when isolated to individual users. True transformation happens when AI is embedded at the organizational level, not just used by a few team members experimenting with prompts.
Recent findings from Google’s DORA metrics report back this up. In 2024, the report focused entirely on AI adoption and its influence on engineering productivity. The conclusion was clear: individual use delivers only marginal gains, but structured, organization-wide implementation can significantly increase output, consistency, and speed, especially in documentation, testing, and internal knowledge sharing.
This is why companies that take AI seriously begin by looking at their internal processes as a whole. Tools like Katara are not meant to serve one user at a time. They are designed to operate across teams, ingesting shared documentation, understanding context from systems like Jira or Confluence, and responding with precision regardless of who asks the question.
We help tech teams go from scattered prompts to structured, scalable agentic systems
Head of Customer Success
For founders: build less, talk more
One of the more grounded messages to come out of the discussion was this: the biggest mistake many engineers make is building before they validate.
The temptation is understandable. Founders are often product-minded, eager to code a solution before deeply understanding whether a problem is truly painful or widespread. However, as James Hotson emphasized, even the most elegant AI solution is wasted if it solves the wrong problem. Or even worse, a problem no one cares about.
Instead of defaulting to prototypes, the smarter approach is to start with structured discovery. Before launching Katara, the team conducted over 50 interviews with companies running online communities. The pattern was consistent: the pain of managing support conversations across platforms like Discord was real, expensive, and growing. Only after confirming this with users did they begin building a solution tailored to that need.
For founders excited about AI agents, the advice is simple. Speak with users. Document the workflows they currently follow. Ask which parts are repetitive, which require judgment, and which are simply too slow. From there, design a system that doesn’t just automate steps, but rethinks how the work could happen if intelligent agents were part of the team from day one.
Betting on agents is a strategy, not a feature
Agentic systems, when implemented correctly, represent a shift in how work is organized: where smart assistants execute tasks in parallel, surface insights in real time, and reduce the operational drag that slows teams down.
Founders who succeed in this space are not the ones who bolt AI onto an existing toolset. They are the ones who design from first principles, treating agents not as novelty, but as contributors. This means thinking carefully about how data flows through a business, how decisions are made, and where repetitive human effort could be redirected toward higher-value problems.
Want the full conversation?
This article captures only a selection of the insights shared during the tech talk between Ralabs and Katara. To dive deeper into practical use cases, product strategy, and how agentic AI is reshaping companies in real time, you can watch the full recording here.
