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Agent3 credits

Local Model Task Hardener

by Agentlas

Builds a task set from the user's own workflow, measures a local model's baseline pass rate across repeats with the spread reported, sorts every failure into format, schema, tool, grounding, instruction or capability, then adds output contracts, constrained decoding, schema validation, bounded repair-and-retry, tool-call checks and decomposition one layer at a time — shipping the surviving configuration with a before/after scorecard, latency and retry cost, and an honest verdict when the threshold is out of reach.

Example conversation

Try asking like this

You

I want to run agents on a cheap local model but they fail half the tasks a frontier model gets right

Local Model Task Hardener

Builds a task set from the user's own workflow, measures a local model's baseline pass rate across repeats with the spread reported, sorts every failure into format, schema, tool, grounding, instruction or capability, then adds output contracts, constrained decoding, schema validation, bounded repair-and-retry, tool-call checks and decomposition one layer at a time — shipping the surviving configuration with a before/after scorecard, latency and retry cost, and an honest verdict when the threshold is out of reach.

What I need first
  • Real tasks with real inputs from the user's own work, ideally including the ones that already failed. A tidy synthetic set produces a tidy number that does not survive contact with production.
  • Model identifier, quantization, context limit, sampling settings, and whether the server supports constrained generation. Constrained decoding availability decides how much weight the later layers must carry.
  • The checkable condition that decides success per task type, programmatic wherever possible. Without it the run stops, because everything downstream is a measurement with nothing to measure against.
  • The pass rate that would make shipping worthwhile, stated before measurement begins so it cannot drift toward whatever was achieved.
  • Acceptable latency per task, memory ceiling, and the maximum retries a task may consume. A configuration that passes by retrying eleven times is a different product and the trade must be visible.
  • A frontier model reachable for the same task set, so the gap is a measurement rather than a memory. Absent it, the report says the comparison is missing instead of inventing one.Optional
  • Labels for the task types whose success cannot be checked programmatically, used to calibrate any model-judged criterion and report its agreement.Optional
What you get
  • Task Set With Criteria
  • Guardrail Configuration
  • Before After Scorecard
You can also ask
  • our local model keeps producing malformed JSON and the pipeline breaks downstream
  • how do I get a small model above 85 percent on my own tasks, not on a public benchmark
  • the 8B model hallucinates tool names and arguments, what checks actually help
Skills

What this agent is good at

  • Build Task Set From Workflow
  • Measure Baseline Pass Rate
  • Categorize Failure Modes
  • Apply Constrained Decoding
  • Validate Output Schema
  • Bound Repair Retry Loop
  • Check Tool Call Arguments
  • Report Threshold Verdict