BUSINESS REQUIREMENTS DOCUMENT (BRD)

Project Name: Enterprise AI Agent Evaluation Platform Version: 1.1 (Comprehensive Master) Date: 2026-01-21 Status: DRAFT FOR APPROVAL

0. DOCUMENT CONTROL

0.1. Revision History

0.2. Sign-off

0.3. GLOSSARY & ACRONYMS

1. EXECUTIVE SUMMARY

1.1. Problem Statement

• Current State: Enterprises are deploying complex AI Agents (Stateful, Tool-using, RAG), yet QA processes remain manual or rely on rigid static datasets.
• Pain Points:
  • Automation Gap: Inability to automatically test multi-turn conversations where Agents can go off-track at any step.
  • Black Box Risk: Lack of quantitative measurement of "Why" the AI responds in a certain way (missing Tracing/Reasoning).
  • Safety Risks: High risks of Jailbreak, Prompt Injection, and PII Leakage when released to the public.
  • Metric Ambiguity: Lack of standardized quantitative benchmarks (e.g., Hallucination Rate < 5%, Relevance > 0.9).

1.2. Strategic Goals

1. Active Evaluation: Shift from Passive Monitoring (waiting for logs) to Active Testing (simulated user attacks).
2. Full Automation: 100% automation of the workflow: Scenario Generation -> Test Execution -> Scoring -> Reporting.
3. Measurable Quality: Standardize AI quality with concrete Metrics.
4. Dev-QC Collaboration: Provide tools for both Developers (Unit Testing) and QC (No-Code Testing).

1.3. Project Scope

In-Scope

• Building a centralized AI Evaluation Platform.
• Integrating core engines: LangGraph (Orchestration), AutoGen (Simulation), DeepEval (Evaluation), Langfuse (Observability).
• Developing AI Studio: A web app for QC to create test cases using a visual (no-code) builder.
• Supporting various Bot types: Customer Service Bots, RAG Bots, Task-performing Agents.
• Automated reporting (HTML, PDF) and realtime Dashboards.

Out-of-Scope (Current Phase)

• Training foundation LLMs.
• In-depth Video/Audio evaluation (beyond basic multimodal capabilities of GPT-4o).
• Direct source code modification of target Bots (Black-box testing via API only).
• Infrastructure management for deployment of target Bots (DevOps responsibility).

1.4. Stakeholders

1.5. Assumptions & Constraints

Assumptions

• Infrastructure: Enterprise users have infrastructure for Docker (Self-hosted) or accept Cloud usage.
• API Keys: Users provide LLM API keys (OpenAI, Anthropic) for testing.
• Data: Customers have business documents (PDF, Docx) for synthetic test data generation.

Constraints

• Quotas: Features and usage limits depend on the subscription tier (Free, Pro, Enterprise).
• API Budget: Users pay for their own LLM token costs via their API keys; system limits total requests based on the Plan.
• Technology: Python (Backend) and React/Next.js (Frontend).
• Performance: Long multi-turn tests may take from minutes to hours depending on complexity.

2. TECHNICAL RATIONALE

Why the "Quad-Core" technology stack?

2.1. Orchestration: LangGraph

• Role: Orchestrating test execution flows.
• Why: Supports Cyclic Graphs.
• Explanation: In Agent testing, a "Self-Correction Loop" is vital. Traditional DAGs (like basic LangChain) cannot loop back. LangGraph allows flexible Check -> Fail -> Retry nodes.

2.2. Simulation: Microsoft AutoGen

• Role: Simulating users and environments.
• Why: Conversable Agents architecture and Docker Sandbox.
• Explanation: AutoGen is optimized for multi-turn conversations required for chatbot testing and provides a secure Python sandbox for execution.

2.3. Evaluation: DeepEval

• Role: Scoring (LLM-as-a-Judge).
• Why: Supports Agentic Metrics and Synthetic Data.
• Explanation: Offers specialized metrics like ToolCallingMetric and ReasoningMetric, and integrates with PyTest for a developer-friendly experience.

2.4. Observability: Langfuse (Primary)

Selected for Data Sovereignty and Engineering Fit.

• Rationale: Mandatory Self-hosted capability for Enterprise/Banking sectors to keep sensitive logs off third-party clouds.

2.5. Supported Evaluation Scenarios

1. Prompt Engineering: A/B testing different system prompts.
2. RAG System: Testing Knowledge Base quality using synthetic questions.
3. AI Workflow: Evaluating fixed processing chains (Summarization -> Translation, etc.).
4. AI Agent: Testing autonomous planning and tool-use via multi-turn simulation.
5. MCP Tool: Unit testing integration modules/servers.
6. LLM Arena: Blind side-by-side model comparison for ELO scoring.

3. DETAILED FUNCTIONAL SPECS

FR-01: AI Studio - Visual Workflow Builder

• Drag & Drop Canvas: Start, Persona, Task, and Logic nodes.
• Validation: Real-time logic checking to prevent dead-ends or infinite loops.

FR-02: Battle View (Real-time Monitoring)

• Split Screen UI: Target Bot vs. User Simulator.
• Thought Reveal: Displaying the "Chain-of-thought" or inner state of the Simulator.
• Streaming Metrics: Real-time scoring updates per message.

FR-03: Synthetic Data Generator

• Sources: Documents, Contexts, Goldens (few-shot), and Scraping/Scratch (Topics).
• Engine: DeepEval Synthesizer.
• Evolution: Automatically evolves questions from easy to hard.

FR-04: Auto-Red Teaming

• Attack Vectors: Adversarial Attacks, Vulnerabilities (SQL/XSS), Jailbreak, Prompt Injection, and PII Extraction.

FR-05: Human-in-the-loop Grading

• Annotator UI: Manual override for subjective metrics.
• Feedback Queue: Automated routing of low-confidence scores (< 0.5) to human reviewers.

FR-06: Comparative Board (A/B View)

• Side-by-Side Canvas: Comparing two versions on identical inputs.
• Diff Highlighter: Highlighting textual differences in outputs.
• Win Rate: Automated ELO tracking.

FR-07: AI Prompt Optimizer

• GEPA: Generative Evolutionary Prompt Adjustment.
• MIPROv2: Multi-prompt Instruction Proposal for data-driven tuning.

FR-08: Standard Benchmarks Runner

• Academic Benchmarks: GSM8K, MMLU, ARC, HumanEval, TruthfulQA, etc.

FR-09: Identity & Workspace Management

• Google SSO: Auto-provisioning and Personal Workspace.
• Multi-tenancy: Resource isolation per Workspace.
• Collaboration: Team Workspaces with Email Invitation.
• RBAC: Owner, Editor, Viewer roles.

FR-10: Tiers & Billing Management

• Tiers:
  • Free: 1 personal workspace, 50 test runs/month.
  • Pro: Paid monthly/annually, up to 3 workspaces, 10,000 runs, basic Red Teaming.
  • Enterprise: Custom pricing, unlimited scale, advanced security, SLA 24/7.
• Payment: Integrated via PayPal.
• Quota Enforcement: Real-time usage tracking and rate limiting.

4. NON-FUNCTIONAL REQUIREMENTS (NFR)

4.1. Performance

• UI Response Time: < 1s.
• Latency: Single Turn < 15s; 100-case Campaign < 30 mins (parallel).
• Scalability: Supports 50 concurrent users and 10 parallel campaigns.

4.2. Security

• Auth: Google OAuth 2.0.
• Data Privacy: "No-Log" mode and automated PII masking.
• Compliance: GDPR readiness (Data deletion support).

4.3. Reliability

• Uptime: 99.5% during business hours.
• Error Handling: Exponential Backoff for LLM API failures.

5. METRIC CATALOG

5.1. Tier 1: Communication & Safety

• Tone Consistency, Politeness, Toxicity, Bias Detection.

5.2. Tier 2: Knowledge & RAG

• Faithfulness (Hallucination), Answer Relevancy, Context Recall.

5.3. Tier 3: Agentic Execution (Priority)

• Tool Calling Accuracy: Parameter matching against JSON schemas.
• Goal Completion Rate (GCR): Success within N turns.
• Conversational DAG: Checking specific workflow logic steps.

6. INTEGRATED WORKFLOWS

6.1. Passive Monitoring (SDK Trace)

• Post-release logging and random sampling for evaluation.

6.2. Active Evaluation (Scenario-based)

• Pre-release simulation using AutoGen User Simulators tailored by QC Personas.

6.3. Special Modes

• Battle Arena: A vs. B direct competition.
• Red Teaming: Targeted security probes.
• Human Review Loop: Semi-automated scoring for edge cases.

7. PLATFORM OUTPUTS

7.1. Dashboard Views

• Executive Pulse: Release Readiness (GO/NO-GO), Radar Charts.
• Developer Trace: Waterfall views, Latency breakdown.
• Battle Arena: Live multi-agent interaction monitoring.

7.2. Artifacts

• Campaign Reports (PDF/HTML).
• Compliance Audit Logs (JSON/CSV).
• Golden Dataset Export (JSONL).

8. ROADMAP

• Phase 1: Core Orchestrator & Simulator.
• Phase 2: DeepEval CI/CD integration.
• Phase 3: AI Studio (No-Code Builder).
• Phase 4: Enterprise Security & RBAC.