docs: professional README with badges, motivation, and reference

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: saviolobo

README.md

@@ -1,9 +1,23 @@
 # Multi-Agent Banking Annotation System
 
+[![Lint](https://github.com/saviolobo/agentic-annotator/actions/workflows/lint.yml/badge.svg)](https://github.com/saviolobo/agentic-annotator/actions/workflows/lint.yml)
+[![Python](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/)
+[![LangGraph](https://img.shields.io/badge/LangGraph-1.2+-orange.svg)](https://github.com/langchain-ai/langgraph)
+[![FastMCP](https://img.shields.io/badge/FastMCP-enabled-purple.svg)](https://github.com/jlowin/fastmcp)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+
 A production-grade multi-agent pipeline for automated intent classification
 on the Banking77 benchmark (13,083 queries, 77 intents), inspired by
 JP Morgan's MAFA system (AAAI 2026).
 
+> **Why this exists:** Manual data annotation costs enterprises ~$0.15/item and
+> doesn't scale. JP Morgan Chase eliminated a 1M-utterance backlog saving 5,000+
+> hours/year using a multi-agent approach (MAFA, AAAI 2026). This project
+> independently reproduces and benchmarks a similar architecture on the public
+> Banking77 dataset, enabling direct comparison against their published results.
+
+---
+
 ## Results
 
 | | This project | JP Morgan MAFA (AAAI 2026) |
@@ -17,43 +31,54 @@ JP Morgan's MAFA system (AAAI 2026).
 > Full pipeline uses 5 agents with MCP-backed guideline retrieval and
 > Redis vector search for similar-example lookup.
 
+---
+
 ## Architecture
 
 Five specialized agents replace manual annotation for high-confidence items
 while routing ambiguous cases to a human review queue.
 
 ![Architecture](docs/architecture.png)
 
-**Key pattern:** LangGraph Send API runs Primary Annotator and Validator
+**Key pattern:** LangGraph Send API runs Primary Annotator Agent and Validator Agent
 in parallel on COMPLEX queries, cutting latency on the most expensive path.
 
 | Agent | Model | Role |
 |---|---|---|
-| Router | Groq llama-3.1-8b-instant | Classify query complexity |
-| Primary Annotator | Cerebras gpt-oss-120b | First-pass label + confidence |
-| Validator | Cerebras gpt-oss-120b | Independent second annotation |
-| Arbitrator | Cerebras gpt-oss-120b | Resolve disagreements |
-| Quality Controller | Groq llama-3.1-8b-instant | Consistency + drift detection |
+| Router Agent | Groq llama-3.1-8b-instant | Classify query complexity (SIMPLE / COMPLEX) |
+| Primary Annotator Agent | Cerebras gpt-oss-120b | First-pass label + confidence + reasoning |
+| Validator Agent | Cerebras gpt-oss-120b | Independent second annotation, blind to Primary |
+| Arbitrator Agent | Cerebras gpt-oss-120b | Resolves disagreements, routes low-confidence to human |
+| Quality Controller Agent | Groq llama-3.1-8b-instant | Consistency checks + label drift detection |
+
+---
 
 ## MCP Servers
 
-Three FastMCP servers provide tools to the agents at inference time:
+Three [FastMCP](https://github.com/jlowin/fastmcp) servers expose tools to agents at inference time:
+
+| Server | Tools |
+|---|---|
+| `guidelines-mcp` | Intent definitions + Redis vector search for similar labeled examples |
+| `label-schema-mcp` | 77 valid intent labels + taxonomy + confusion-prone pairs |
+| `human-review-mcp` | SQLite-backed queue for low-confidence items awaiting human review |
 
-- **guidelines-mcp** — intent definitions + Redis vector search for similar labeled examples
-- **label-schema-mcp** — 77 valid intent labels + taxonomy
-- **human-review-mcp** — SQLite-backed queue for low-confidence items
+---
 
 ## Stack
 
 | Layer | Technology |
 |---|---|
 | Orchestration | LangGraph + SqliteSaver checkpointing |
 | MCP servers | FastMCP |
-| Vector search | Redis Stack + sentence-transformers |
+| Vector search | Redis Stack + sentence-transformers/all-MiniLM-L6-v2 |
 | API | FastAPI |
 | Frontend | Next.js 15 + shadcn/ui + Recharts |
+| Tracing | Langfuse (self-hosted) |
 | Dataset | Banking77 — PolyAI (CC-BY 4.0) |
 
+---
+
 ## Setup
 
 ```bash
@@ -66,6 +91,8 @@ uv run python main.py       # API on :8000
 cd frontend && npm run dev  # Dashboard on :3000
 ```
 
+---
+
 ## Eval
 
 ```bash
@@ -80,3 +107,16 @@ caffeinate -i uv run python -m eval.run_eval \
 caffeinate -i uv run python -m eval.retry_errors \
   --input eval_results.json --sleep 60
 ```
+
+---
+
+## Reference
+
+Inspired by: *MAFA: Multi-Agent Framework for Annotation*, JP Morgan Chase, AAAI 2026.
+Dataset: [PolyAI/banking77](https://github.com/PolyAI-LDN/task-specific-datasets) — CC-BY 4.0.
+
+---
+
+## License
+
+MIT © [Savio Lobo](https://github.com/saviolobo)