back_office_ops · saas · workflow
Hard-Earned Lessons from a Year of Building AI Agents at IBM Research
Non-experts could not translate generative AI capabilities into solving high-value problems, while only teams with deep LLM and systems engineering expertise could unlock AI's potential—leaving a broad set of everyday builders behind.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Non-expert AI gap identified
The team observed that non-experts struggled to capture generative AI's productivity gains and formed a hypothesis about empowering a broader user base.
Tools used
BeeAI FrameworkLlama 3-70B-ChatRAGReActAgent
Outcome
IBM Research open-sourced BeeAI Framework and quickly found an audience with TypeScript developers, with standout community implementations including Bee Canvas and UI Builder emerging.
Results
Running sinceEarly 2024
Source
https://medium.com/@mayamurad/hard-earned-lessons-from-a-year-of-building-ai-agents-945d90c78707
Grounding & classification
Source type: technical build writeup
19 fields verified against source quotes.
agentic workflowai agentragknowledge basebuilder submittedfailure mode describedproduction runtime claimedtools describedworkflow describedsoftwareemployee productivitytechnical build writeupback office opsagentic task execution