Foundation model - Observability & Evaluation
LLM outputs are inherently non-deterministic, so what "works in a demo or MVP" often fails in production — in fact, most AI agents never successfully reach production. Human intuition and prompt engineering alone don't scale without systematic measurement and holistic evaluation, especially for RAG systems. To achieve enterprise trust, organizations must focus on compliance, auditability, cost and latency optimization and deep observability across agentic workflows.
Key Features of Pi-LangEval for Next-Generation Agent Monitoring
Agentic AI Evaluation Platform
Auto-Discovery of Agent Architectures
ARCHITECTUREThe system automatically inspects trace patterns to discover "Agents" and infers their black-box structure (Inputs, Outputs, and Child Tools) without manual configuration.
Why It Matters
Unlike other evaluation platforms with passive tracing, this actively "reverse engineers" your agent's behavior, making it easier to understand complex, evolving agentic workflows without manual intervention.
Unified "Write Once, Run Anywhere" Evaluation Configs
CI/CDEvaluation Config allows a single evaluation template to be targeted dynamically at either Live Tracing (production) or Dataset Runs (testing) with a simple toggle.
Why It Matters
Eliminates the need to maintain separate "Testing" and "Monitoring" logic. Your CI/CD tests can become your production monitors instantly, reducing maintenance overhead and ensuring consistency.
Curated Evaluator Library (Clone-to-Own)
MARKETPLACEA built-in "Marketplace" of system-level Library Templates that users can browse and "Clone" into their project with one click, inheriting best-practice prompt engineering.
Why It Matters
Lowers the barrier to entry for evaluation. Users don't start from scratch; they start with industry-standard evaluators (Hallucination, Conciseness, etc.), accelerating time-to-value.
Smart Evaluation Triggers (Context-Aware Filters)
AUTOMATIONThe creation logic includes "Smart Filter Detection" which automatically targets evaluations to specific spans (e.g., "Agent" vs "Tool" spans) based on the semantic content of the prompt.
Why It Matters
Reduces misconfiguration. The system "knows" that a generic "Tool Usage" metric should only run on Tool spans, not whole traces, ensuring accurate and relevant evaluations.
Granular "Shadow" Evaluation with Delays
ASYNCBuilt-in support for delay seconds in evaluation configs, allowing "Shadow Mode" evaluations that handle eventual consistency or long running async agent tasks.
Why It Matters
Critical for Agentic workflows where the "result" might settle seconds after the trace finishes. Prevents race conditions in scoring and ensures evaluation accuracy for delayed operations.
Strict-Schema Dataset Management
QUALITYDatasets support explicit input schema and output schema mapping/validation, treating datasets as "Golden Samples" with strict contracts rather than just loose CSV uploads.
Why It Matters
Ensures high-quality testing data. Prevents "Garbage In, Garbage Out" in evaluation runs by validating data structure before execution, maintaining dataset integrity and reliability.
Customizable Pricing Engine
ENTERPRISEA dedicated Model Pricing table allows users to override input/output costs per model/provider, supporting custom fine-tuned models or negotiated enterprise rates.
Why It Matters
Essential for enterprises using proxies or private models where public API pricing doesn't apply. Enables accurate cost tracking and budgeting for custom deployments.
Native Multi-Tenancy & Project Isolation
SAASThe architecture is built from the ground up with Project and User isolation boundaries enforced at the API and Database level.
Why It Matters
Makes the platform ready for SaaS-style deployment or internal Platform Engineering teams serving multiple business units out of the box, with zero additional configuration.
LLM - Observability & Evaluation
WHY
- LLM outputs are non-deterministic
- "Works in demo" ≠ "Works in production" - 90% of AI agents don't make it successfully to production
- Human intuition does not scale – can test for 10 to 100 prompts
- Prompt engineering without measurement is guesswork
- RAG systems must be evaluated holistically
- Compliance, audit and enterprise trust
- Cost and latency optimization
- Agentic workflows need deep observability
WHAT
- Evaluation frameworks bring repeatability to non-deterministic systems
- This protects production rollback
- This enables scale without human burnout
- This turns prompt engineering into an engineering discipline
- Customers discover whether the problem is retrieval, context or generation
- This is often a procurement requirement
- Customers save money while improving quality
- Without this, agents are a black box
HOW
- Safety and Responsibility – Bias and Toxicity
- Quantitative – BLEU, ROUGE
- RAG - Context Precision
- Agentic AI with custom evaluation
- Qualitative (LLM as a Judge) - Answer Relevance, Faithfulness, Conciseness