The question I kept coming back to: Is there a tool that automates the low-skill, high-repetition parts of our AI workflow?
Because after you ship a few real RAG/agent systems, you notice the time sink isn’t the model. It’s the scaffolding you rebuild every time.
The repetitive glue work that quietly dominates AI engineering
Most teams end up re-implementing the same reliability layer:
1. Deterministic ingestion
Small extraction changes --> different text --> different embeddings --> different retrieval. You didn’t change the model, but the system behaves differently.
2. Chunking + metadata + ID hygiene
Chunk policies drift. Metadata becomes inconsistent. IDs aren’t stable. Then retrieval becomes unpredictable and debugging becomes guesswork.
3. Schema validation (so JSON doesn’t break your pipeline)
Once LLM output flows into tools, you’re in strict input required territory. One malformed JSON response can collapse a workflow.
4. Tool contracts + retries + timeouts + fallbacks
Tools behave like flaky microservices: rate limits, network hiccups, partial failures. Everyone ends up writing the same wrappers.
5. Evaluation harnesses + baselines + regression checks
Without evals, you can’t distinguish improved from changed. You also can’t catch breakage before users do.
6. Logging + traces (so failures are diagnosable)
Multi-step workflows fail in non-obvious ways. Without traces, you can’t replay what happened or isolate the step that introduced failure.
None of this is the deep skill work engineers want to spend creativity on, but it’s the work that decides whether your agent is a demo or a dependable system.
Deep skill vs repetitive steps (the split that clarified everything)
Here’s the mental model we use:
Deep skill (human-owned):
• defining goals + constraints
• deciding what “good” means
• choosing metrics and tradeoffs
• designing gold/adversarial tests
• making product and safety decisions
Repetitive steps (automation-owned):
• ingestion --> chunking --> metadata normalization --> embedding --> indexing
• schema validation + repair loops
• tool contracts + retries/backoff/timeouts/fallbacks
• baseline eval runs + regression diffs
• logging/tracing + replay artifacts
A simple architecture map
Think of it like this:
Human intent --> Gather project specs --> Define standards & structures --> Evals/Reports --> Human judgment
Automation handles the repeatable scaffolding. Humans keep the meaning.
What we’re building (softly)
That’s the bet behind HuTouch: not to replace engineering, but to automate the repetitive glue so engineers can focus on the parts that actually require judgment.
The discussion question:
If you could automate one part of your AI workflow end-to-end, what would you pick first: ingestion determinism, chunking/metadata, schema validation, tool reliability, eval regressions, or tracing/replay?
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