From Bots To Agents: Building Trustworthy Autonomy With Hakkōda, an IBM Company

Sep 13, 2025

Atalia Horenshtien, an AI and data strategist at Hakoda, shares insights from her rich experience as a former data scientist and software engineer. She tackles the complexities of transitioning from traditional bots to advanced AI agents. Atalia emphasizes the importance of human oversight and a foundational understanding of AI's capabilities. She offers grounded advice like building observability layers and focused cost tracking for performance measurement. The conversation highlights AI as an enabler for innovation rather than a threat to jobs, making it relevant for leaders navigating technological change.

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INSIGHT

What Makes An Agent Different

AI agents differ from scripted bots because they hold goals, understand ambiguous language, and can learn from feedback.
They make decisions on the fly rather than following fixed if-then rules.

ADVICE

Start Internally And Add Guardrails

Start agent projects internally where you can monitor behavior and contain mistakes.
Build observability, human-in-the-loop review, and role-based access to guardrail models and data.

ADVICE

Automate Repetition, Humanize Judgment

Automate repetitive, well-patterned tasks and always include human oversight for edge cases.
Feed agents proprietary examples and keep humans in the loop for complex judgment calls.

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I invited Atalia Horenshtien to unpack a topic many leaders are wrestling with right now. Everyone is talking about AI agents, yet most teams are still living with rule based bots, brittle scripts, and a fair bit of anxiety about handing decisions to software. Atalia has lived through the full arc, from early machine learning and automated pipelines to today’s agent frameworks inside large enterprises. She is an AI and data strategist, a former data scientist and software engineer, and has just joined Hakoda, an IBM company, to help global brands move from experiments to outcomes. The timing matters. She starts on the 18th, and this conversation captures how she thinks about responsible progress at exactly the moment she steps into that new role.

Here’s the thing. Words like autonomy sound glamorous until an agent faces a messy real world task. Atalia draws a clear line between scripted bots and agents with goals, memory, and the ability to learn from feedback. Her advice is refreshingly grounded. Start internal where you can observe behavior. Put human in the loop review where it counts. Use role based access rather than feeding an LLM everything you own. Build an observability layer so you can see what the model did, why it did it, and what it cost.

We also get into measurements that matter. Time saved, cycle time reduction, adoption, before and after comparisons, and a sober look at LLM costs against any reduction in FTE hours. She shares how custom cost tracking for agents prevents surprises, and why version one should ship even if it is imperfect. Culture shows up as a recurring theme. Leaders need to talk openly about reskilling, coach managers through change, and invite teams to be co creators. Her story about Hakoda’s internal AI Lab is a good example. What began as an engineer’s idea for ETL schema matching grew into agent powered tools that won a CIO 100 award and now help deliver faster, better outcomes for clients.

There are lighter moments too. Atalia explains how she taught an ex NFL player the basics of time series forecasting using football tactics. Then she takes us behind the scenes with McLaren Racing, where data and strategy collide on the F1 circuit, and admits she has become a committed fan because of that work.

If you want a practical playbook for moving from shiny demos to dependable agents, this episode will help you think clearly about scope, safeguards, and speed. Connect with Atalia on LinkedIn, explore Hakoda’s work at hakoda.io, and then tell me how you plan to measure your first agent’s value.

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