The Input Problem: Why Garbage In Doesn't Just Mean Garbage Out

AI tools don't fail because of bad models — they fail because of bad data. This episode breaks down the three input problems that silently corrupt AI outputs and the practical design moves that fix them before the agent ever runs.

Show Notes

Most teams evaluating AI performance look at the outputs — the writing, the decisions, the formatting. But ViBe's latest episode argues that the real failure point sits much earlier in the pipeline: in the data handed to the agent before a single instruction is processed. "The Input Problem" reframes AI reliability as a data design challenge, and makes the case that confidence in your tools has to be built upstream, not patched downstream.

The episode walks through three distinct categories of bad input — each subtle, each capable of producing AI outputs that look polished while being fundamentally wrong — and then lays out three concrete design moves teams can implement to close the gap. Here's what's covered:

• Incomplete input: Half-filled forms, context-light handoffs, and truncated documents leave agents to fill gaps with inference — which is just a structured guess with no grounding.
• Stale input: When agents pull from databases that haven't been updated, they reason confidently about a version of reality that no longer exists.
• Ambiguous input: Inconsistent conventions across teams — like "complete" meaning different things in different departments — give agents no way to distinguish signals they're treating as equivalent.
• Why this is a data hygiene problem, not an AI problem: Humans used to absorb bad input quietly; agents amplify it at speed, making the underlying issues newly visible and newly expensive.
• Treating input as a designed artifact: Building workflow automation that audits source data — who creates it, how often it's updated, where the known inconsistencies live — before the agent ever runs.
• Validation gates and transparent outputs: Adding pre-agent checks for required fields and data freshness, and designing agents to surface the key inputs they used so human review becomes a quick scan rather than a guessing game.

The episode's central argument is that an AI agent is an execution system, not a corrective one — it does things consistently given what it receives, for better or worse. Teams building custom internal tools that hold up operationally (not just in demos) need to design the input layer with the same care they give the agent logic itself. For a broader look at how this fits into a complete build, how the build process works is a useful reference. More from the show: if this episode resonated, The Approval Bottleneck: How to Let Agents Move Without Losing Control covers a related challenge — giving AI tools autonomy without sacrificing oversight.

VB.co

What is ViBe?

Building the connective tissue of a modern business software stack — shared identity, module boundaries, tenancy models, and the integration decisions that are cheap now and expensive later.

Each episode takes one architectural decision — where a tenant boundary belongs, how modules should hand off a signed-in user, what to share and what to duplicate — and reasons through the consequences a year out. Written for platform engineers and architects designing systems other teams will build on. Five or six minutes, one decision, no framework advocacy.

Topics include tenancy models and boundaries, shared identity and single sign-on between modules, what to share versus duplicate, module contracts and versioning, migration paths, and integration decisions that are cheap now and expensive later.

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