Anastasiia Korneva

One thing I’ve learned is that keeping any AI agent or smth similar “on-track” is very similar to setting up automation tools. For example, when configuring a Linked Helper scraper for LinkedIn you define boundaries, specify exactly what data to collect, and create stop conditions. The same concept applies to conversational AI: without clear parameters, it will drift or over-generate. I think the best method is to break complex interactions into smaller, well-defined checkpoints. Instead of giving one massive instruction, create a sequence of micro-goals that the agent must validate before moving forward. This way, if it loses focus, you can roll it back to the last checkpoint rather than restarting the entire flow. Another important factor is context management coz keeping the history lean and only passing relevant details ensures the agent won’t get overwhelmed. I also recommend using reusable prompt templates that standardize tone, style, or task expectations coz this consistency keeps the agent aligned. Finally, don’t forget about feedback loops. Just as you’d review data quality from a scraper to ensure it’s accurate, with AI you can introduce validation steps or user confirmations at key points. That way, the system self-corrects before going too far off-track.