Building Software with AI: Why a Simple Task Can Go Wrong @ai-science
Building Software with AI: Why a Simple Task Can Go Wrong  @ai-science
Uploaded August 2025 | Updated September 2026, 2 weeks ago
When designing an agentic workflow, it often looks simpler than it is. In this demo, our flow involves three core steps: collect, process, deliver. For example, pulling Slack messages from a specific channel requires more than just asking an LLM to “get the data.” If you don’t specify the constraints, the model will assume you have admin access and try to build through a Slack app, only to run into permissions issues later. Without clear instructions, the agent will make assumptions on your behalf, which might not align with reality. Clarifying the actual steps helps you avoid wasted effort and too much clean up.

#AIagents #WorkflowAutomation #SlackIntegration #LLM #AgenticSystems
Building Software with AI: Why a Simple Task Can Go WrongXAI for LLMs: looking under the hood of Large Language ModelsDartboard Analogy: Optimizing Retrieval with Uncertainty in RAGBuilt Multi-agent LLM Products - Bootcamp TeaserWorking Smarter: How I Leverage AI to Create My WorkflowFrom Brainstorm to Working Prototype in HOURS Meet IdeaStormWhy Evaluation of AI Agents Matters: Confidence, Control & Shipping FasterHow Do You Validate LLM Systems Beyond Benchmarks?Intersection Between LLMs and Products
LLMs Explained - Aggregate Intellect - AI.SCIENCE |

Building Software with AI: Why a "Simple" Task Can Go Wrong

SHARE TO X SHARE TO REDDIT SHARE TO FACEBOOK WALLPAPER