Decision models vs LLM‑as‑a‑judge in Amazon Bedrock AgentCore Evaluations

In a recent Amazon blog post, Amazon Bedrock AgentCore Evaluations added support for decision‑model back‑ends such as TypeSafe’s Jev and the open‑source Strands Decider 2B. The feature matters because it lets teams replace noisy, free‑form LLM scores with calibrated probabilities for narrow, repeatable questions—something that can make automated agent monitoring more reliable.

AgentCore Evaluations at a glance

AgentCore Evaluations scores an agent throughout its lifecycle: on‑demand during development, in batch jobs against stored sessions, and live against production traffic. It ships with 15 built‑in evaluators that use a general‑purpose LLM to produce a label and a textual rationale. Developers can also register custom evaluators, either as an "LLM‑as‑a‑judge" (another LLM that follows a rubric) or as a code‑based evaluator that runs inside an AWS Lambda function. The code‑based path is where decision models plug in.

How a decision model differs from an LLM evaluator

A decision model is a small, purpose‑built model that:

• Takes a fixed‑structure input (the agent’s OpenTelemetry span).
• Returns a probability distribution over a predefined answer set.
• Provides a calibrated confidence score that can be thresholded directly.

By contrast, an LLM evaluator produces a free‑form score plus a rationale. The rationale is useful for debugging, but the confidence is implicit and can drift between calls. Decision models therefore excel when you need a repeatable binary, multi‑choice, or ordinal answer—e.g., “Was the response grounded?” (yes/no), “How helpful was the response?” (4‑level score), or “Did the agent complete the user’s goal?” (choice of three outcomes).

Jev and Strands Decider 2B in practice

The blog demonstrates two back‑ends:

• Jev – a hosted decision model from TypeSafe accessed via an API key.
• Strands Decider 2B – an open‑source 2‑billion‑parameter model you can run on an EC2 instance or locally.

Both support three question types (noul, choice, score) and can be added to the evaluation pipeline with a single flag when running the provided evaluate.py script. The Lambda wrapper normalises the OpenTelemetry span, calls the model, and maps the probability output back to the AgentCore result format.

When a decision model is the right tool (analysis)

The trade‑off most teams overlook is coverage vs. certainty. Decision models give you a crisp probability, but they only answer questions that fit a fixed rubric. If the rubric needs nuance—e.g., “Explain why the response is partially grounded”—an LLM judge still wins because it can generate reasoning and handle edge cases the model was never trained on.

In practice this means:

• Use a decision model for screening checks that are frequent and easy to formalise (policy compliance, length limits, binary safety flags). The model’s threshold can gate traffic into a deeper LLM‑based review.
• Reserve LLM‑as‑a‑judge for investigative checks where you need to surface evidence, trace missing information, or produce an audit trail.
• Validate any decision model on a labelled sample of your own traffic before you trust it for release gating. The blog warns that reframing a rubric often tightens or loosens the measurement; only a side‑by‑side run against human labels will reveal the drift.
• Keep an eye on calibration drift. Even a well‑trained decision model can become mis‑aligned as your agent’s behaviour evolves, so schedule periodic re‑validation (quarterly is a common cadence).

Quick start: add a decision‑model evaluator today

1. Prerequisites – Ensure you have an AWS account in a region where AgentCore Evaluations is available, CloudWatch Transaction Search enabled, and a Bedrock foundation model ready.
2. Clone the sample – git clone https://github.com/aws-samples/bedrock-agentcore-evaluations.git and cd bedrock-agentcore-evaluations.
3. Install – pip install -r requirements.txt (Python 3.12+ required).
4. Pick a backend –
   • For Jev, request a TypeSafe API key at typesafe.ai and note the secret ARN.
   • For Decider, launch the provided Docker image or an EC2 instance using the --decider-ec2 flag.
5. Deploy evaluators – Run python evaluate.py --with-jev --jev-secret-arn <ARN> (or --with-decider). The script will create the Lambda role, package the evaluator code, and register three evaluators (Groundedness, Helpfulness, GoalCompletion).
6. Run a sanity check – The script executes five labelled scenarios and prints the probability scores alongside the built‑in LLM scores. Look for obvious mismatches; adjust the question phrasing or threshold until the model aligns with your human labels.
7. Add to production – Once satisfied, add the flag to your CI/CD pipeline so every new build of the agent automatically registers the decision‑model evaluators.

By the end of this hour you’ll have a deterministic, threshold‑ready evaluator running side‑by‑side with the default LLM judges. From there you can experiment with tighter thresholds, combine multiple decision models, or roll them into a monitoring dashboard.

Sources

• Use decision models with Amazon Bedrock AgentCore Evaluations