Agent Evaluation Framework

The Agent Evaluation Framework provides tools for measuring and improving agent performance, ensuring consistent quality across different use cases.

Current Status

Status: Phase 1 Implemented

Phase 1 of the custom AgentDock Core Evaluation Framework has been implemented. This includes the core runner, evaluator interface, storage provider concept, and a suite of initial evaluators (RuleBased, LLMJudge, NLPAccuracy, ToolUsage, Lexical Suite).

Overview

The framework offers:

• Extensible Architecture: Based on a core Evaluator interface.
• Suite of Built-in Evaluators: Covering rule-based checks, LLM-as-judge, semantic similarity, tool usage, and lexical analysis.
• Configurable Runs: Using EvaluationRunConfig to select evaluators and criteria.
• Aggregated Results: Providing detailed outputs with scores, reasoning, and metadata.
• Optional Persistence: Basic file-based logging (JsonFileStorageProvider) implemented, with potential for future integration with a Storage Abstraction Layer.

Architecture (Phase 1 Implementation)

Implementation Options

A Custom Implementation within AgentDock Core was chosen and developed for Phase 1. This provides:

• Full control over the evaluation process.
• Tight integration with AgentDock types (AgentMessage, etc.).
• Specific evaluators tailored to agent use cases (e.g., ToolUsageEvaluator).
• An extensible base for future enhancements.

Third-party integrations were deferred to allow for a bespoke foundation matching AgentDock's architecture.

Key Components (Phase 1)

• EvaluationInput: Data packet including response, prompt, history, ground truth, context, criteria.
• EvaluationCriteria: Defines metrics with name, description, scale, and optional weight.
• Evaluator Interface: Core extensibility point (type, evaluate method).
• EvaluationResult: Output per criterion (score, reasoning, type).
• EvaluationRunConfig: Specifies evaluators, their configs, optional storage provider, metadata.
• EvaluationRunner: Orchestrates the run via runEvaluation function.
• AggregatedEvaluationResult: Final combined output with overall score (if applicable), individual results, snapshots.
• JsonFileStorageProvider: Basic implementation for server-side result logging.

Key Features (Phase 1)

• Rule-Based Checks: Length, includes, regex, JSON validity.
• LLM-as-Judge: Qualitative assessment via LLM call with templating.
• Semantic Similarity: Cosine similarity using pluggable embedding models (default provided).
• Tool Usage Validation: Checks tool calls, arguments against expectations.
• Lexical Analysis: Similarity (Levenshtein, Dice, etc.), keyword coverage, sentiment (VADER), toxicity (blocklist).
• Flexible Input Sourcing: Evaluators can pull text from response, prompt, groundTruth, or nested context fields.
• Score Normalization & Aggregation: Runner attempts to normalize scores to 0-1 and calculate weighted average.
• Basic Persistence: Optional JSONL file logging.
• Comprehensive Unit Tests: Added for core components and evaluators.

Benefits (Achieved in Phase 1)

1. Foundational Quality Assurance: Basic framework for consistent checks.
2. Extensible Base: Custom evaluators can be built.
3. Initial Benchmarking: Enables comparison of runs via results.
4. Concrete Metrics: Moves beyond subjective assessment for core areas.

Timeline

Phase	Status	Description
Approach Evaluation	In Progress Completed	Comparing third-party vs. custom solutions Custom solution chosen.
Architecture Design	Planned Completed	Phase 1 architecture designed and implemented.
Core Implementation	Completed (Phase 1)	Basic framework, runner, interface, initial evaluators, storage provider implemented.
Phase 2 / Advanced Features	Planned	See PRD for details (e.g., Advanced evaluator configs, UI integration, enhanced storage, etc.).

Use Cases

Agent Development

Apply evaluations during development to iteratively improve quality:

The implemented Phase 1 framework provides the core capabilities for this loop. Refer to the Evaluation Framework PRD for detailed usage and Phase 2 plans.