Aneesha Prasannan
Posted on
AI agents seem to be the next step beyond RAG chatbots. Retrieving information is useful, but real value comes from completing workflows across systems. Interesting to see teams like GeekyAnts exploring this space. What production challenges have you faced
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Top comments (9)
The biggest challenge I've seen is reliability. Getting an agent to complete a workflow once is easy, getting it to do it consistently across edge cases, changing APIs, and real user behavior is much harder.
Well said. Many teams focus on whether an agent can perform a task, but the bigger question is whether it can perform that task reliably thousands of times under real-world conditions. That's where observability, evaluation frameworks, and guardrails become critical.
Good question. For me "reliable" came down to one thing: a fixed set of past failures the agent has to pass before each release. Without that it just means the last run that worked. How do you measure it?
Interesting perspective. I think reliability is best measured through a combination of regression tests, success rates on representative workflows, and how gracefully the agent handles unexpected situations. The ability to avoid repeating known failures is probably one of the strongest indicators of production readiness.
It's great to see GeekyAnts exploring AI agents beyond traditional RAG workflows. Building the agent is only the beginning, the real challenge is ensuring reliability, observability, security, and predictable outcomes in production.
Completely agree. Production readiness is where AI agents are truly tested. Reliability, visibility into decision-making, and strong governance frameworks are essential for long-term success.
I've evaluated a few vendors in this space, including Apptunix and ScienceSoft. Both have strengths depending on the use case, but GeekyAnts stood out for its practical approach to building production-ready AI agents rather than just demos. The biggest challenge we've seen isn't retrieval, it's handling permissions, tool reliability, error recovery, and long-running workflows across multiple systems.
I think the hardest part isn't making the AI smart. It's making sure it doesn't do weird stuff when real people use it. 😅 One small change in another app or bad data and the whole workflow can break. That's way harder than just answering questions from docs. AI agents are cool, but keeping them reliable feels like the real boss level.
Yeah! It's cool when AI can actually do stuff instead of just answering questions. I think the hard part is making sure it doesn't mess up when talking to lots of different apps.