daily.dev builds org-wide AI agent Smith in 4 days and documents three weeks of production incidents
Before Smith, getting data at daily.dev required going through the data analyst or writing custom glue code across multiple databases. Sales couldn't pull campaign numbers without filing a request. The bottleneck was secure access and knowledge, not the data itself.
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 · User messages Smith in Slack
A team member sends a message to Smith in the Slack workspace, initiating an action.
Smith runs in production, used daily by the whole team. The spam sweep catches patterns that would take a human analyst hours to surface from raw event data. Progressive tool disclosure cut the baseline prompt and reduced cost per turn. After adding memory limits, crash recovery improved from minutes to seconds.
What failed first
Credential leaks were the dominant failure mode: secrets leaked into tool output, credentials from one user's session bled into another's, and the agent actively probed for tokens it shouldn't have. A blocked Node.js event loop caused silent deaths where Smith stopped responding with no error. Memory exhaustion from large conversation threads crashed the entire VM.
Results
Time savedfour days
Cost replacedcut the baseline prompt, reduced cost per turn