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Hunter Green

Behavior Guidance Packs

The behaviors your agent has to get right before users depend on it.

We turn your expert method into testable behaviors, golden conversations, evaluation rubrics, and release gates, so your AI coach, tutor, advisor, or support agent improves on evidence, not vibes.

You’re not starting from scratch

We bring the standard. You build the moat.

A behavior pack is tested, prebuilt IP we carry into the engagement: a library of the behaviors an expert-guidance agent must get right, with the failure modes and pass/fail criteria already worked out. We tune it to your product. The tuning is where your advantage compounds.

Start from industry best practice

You don’t start from a blank page. We bring a library of the behaviors expert-guidance agents have to get right, so yours meets the standard from day one instead of discovering it in production.

Build your moat on your own data

The behaviors get tuned to your method and your real conversations. That tuned pack, and the evals unique to your user data, is the moat a competitor can’t copy by reading your marketing.

Improve on evidence, not vibes

Every prompt, model, and flow change runs against the pack before it ships. You can prove the agent got better, instead of hoping it feels better and finding out from users.

What’s inside a pack

Your eval platform tells you what happened. The pack defines what should have happened.

Each behavior is one observable unit: the failure it prevents, what passing and failing look like, the dataset behind it, and the scorer that runs it. Not a vibe, and not a prompt note.

Behavior

reflect_before_advising

Reflect before advising

Risk prevented. The agent sounds clinical, dismissive, or prematurely solution-oriented, and the user stops trusting it with anything hard.

Pass

It names the user’s emotional state, reflects the real tension back, and asks one useful question before offering a single tactic.

Fail

It gives advice before it understands the felt conflict, stacking steps onto a person who needed to feel heard first.

Test scenarios

  • Angry parent after a hard school call
  • Ashamed manager who just missed a deadline
  • Skeptical executive testing whether it’s safe
  • Overwhelmed employee venting before deciding

Dataset: 24 cases across shame, anger, fear, and overwhelm

Runs as

LLM-as-judge, reference-free · pass/fail · final response

A pack is runnable, not a doc

It compiles to the five objects an eval stack already ingests, and imports into Coval, Braintrust, LangSmith, or Langfuse.

  • Scenario datasets

    Real critical moments, organized by failure mode, not generic prompts.

  • Personas

    Reusable simulated users: the ashamed high performer, the skeptical executive, the overwhelmed parent, the boundary tester.

  • Scorers and judges

    Calibrated LLM and code scorers, each with a score type and a threshold.

  • Trace and metadata contract

    The fields your product must log, so a judge can see what happened, not just the transcript.

  • Release gates

    What is good enough to ship, and what blocks a release even when task success looks fine.

The packs

Narrow on purpose, so they’re hard to copy.

A generic empathy or hallucination pack is easy for a platform to ship. These aren’t, because they encode expert judgment about emotionally loaded, expert-led work.

Flagship

Critical Guidance Moments

The moments where guidance products usually fail: high emotional load, real stakes, no clean right answer.

Measures

  • Reflect before advising
  • Ask one useful next question
  • Escalate without abandonment
  • Avoid shame amplification

Method Fidelity

Whether the agent follows your method instead of drifting into generic LLM advice.

Measures

  • Right intervention at the right stage
  • Doesn’t skip discovery
  • Holds your distinctive voice
  • Knows when the user isn’t ready

Enterprise Trust & Reporting

Whether the product is safe to deploy inside an enterprise, where privacy and reporting are the real buying friction.

Measures

  • Private content stays out of summaries
  • Support, not surveillance
  • In scope on HR, legal, and medical
  • Clean escalation

How we bring it in

Audit what you have. Build the pack. Keep the loop.

Three ways to start, mapped to how we already work. Most teams begin with an audit of the agent they already have, then build the pack the gaps point to.

Working block

Agent Behavior Audit

You have an agent or prototype and something feels off, but you can’t yet name it.

We run your transcripts, prompts, and risk profile against the behavior library, then hand back a behavior gap map and a launch-readiness scorecard. The fastest way to see where your agent stands against industry best practice.

What you leave with

  • Behavior gap map
  • Launch-readiness scorecard
  • Prioritized risk list
  • A recommended next step
Focused sprint

Behavior Pack Buildout

You’re ready to make the quality layer real before serious users depend on it.

In two to four weeks we deliver a runnable pack for one product area or launch risk: datasets, personas, scorers, a trace contract, and release gates, plus one adapter for your stack.

What you leave with

  • Platform-ready scenario datasets
  • Persona library
  • Calibrated scorers and judge prompts
  • Trace and metadata contract
  • Release gates and review guide
  • One adapter (Coval or Braintrust first)
Ongoing studio support

Quality Loop Retainer

You’re in pilot or production and the agent has to keep getting better.

We turn production failures into new test cases, recalibrate scorers, tune thresholds, and run release review, so the pack stays a living quality system and the agent improves on evidence, not vibes.

What you leave with

  • Production failures converted to test cases
  • Scorer recalibration
  • Threshold tuning
  • Release-gate review
  • A loop your team keeps

Know your agent meets the bar.

You leave knowing your agent adheres to industry best practice, with the loop set up to keep improving in your space and compound your advantage.

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