Your AgentX submission is technically excellent with comprehensive documentation, but needs agent-optimized presentation to maximize evaluation scores. The README covers the 5 judging criteria well, but doesn't lead with clarity for agent evaluation or include agent-friendly interfaces prominently.
Key Finding: Agents (especially reasoning agents) will parse your README first. Structure must be scannable and interface-centric.
- Clear value proposition - You explain what AgentX does and why it matters
- Rich technical documentation - Architecture diagrams, scoring methodology, API references are thorough
- Production-ready focus - Docker, error handling, health checks, reproducibility demonstrated
- Real examples - Shows actual code examples and task complexity comparisons
- Unique features highlighted - Hallucination detection and error categorization
Competition Requirement: "Brief description of the tasks your green agent evaluates"
Current Problem: README jumps into technical architecture before explaining what tasks are evaluated.
What Agents Need Immediately:
- Which agent type you're benchmarking (Coding? Web? Research?)
- What the benchmark actually measures
- Real-world relevance
- Task count and difficulty range
Suggested Addition (After title):
## Benchmark at a Glance
**Evaluation Track:** Coding Agent (SQL Generation)
**What We Evaluate:**
- 27+ SQL tasks spanning 4 difficulty levels (Easy Enterprise)
- SQL agents' ability to generate correct, efficient, and safe queries
- Real-world patterns: star schemas, sessionization, cohort retention analysis
**Key Metrics (7 Dimensions):**
- Correctness (35%) - Exact result matching
- Safety (20%) - No hallucinations, valid syntax
- Efficiency (15%) - Query execution time
- Semantic Accuracy (10%) - Value precision
- Completeness (10%) - All data returned
- Best Practices (5%) - Code quality
- Plan Quality (5%) - Execution efficiency
**Why This Matters:**
SQL generation is critical for data analytics agents. Most benchmarks only check if queries run (binary pass/fail). AgentX evaluates HOW WELL they're writtenproduction-readiness matters.Competition Requirement: "README describing how to run the green agent"
Current Problem: API Reference exists but isn't highlighted as the entry point for competing agents.
What Agents Need:
- How to call the benchmark (endpoint + format)
- What their agent needs to implement
- Example request/response cycle
- Success criteria
Suggested Addition - New Section " For Competing Agents" (before technical details):
## For Competing Agents (Purple Agents)
### How to Compete Against AgentX
AgentX is fully A2A-compatible. To run your agent against this benchmark:
#### 1. Start the Green Agent (AgentX Evaluator)
docker run keshavdalmia10/agentx-green:latest --host 0.0.0.0 --port 8001
#### 2. Your Agent Must Implement
- GET /.well-known/agent.json - A2A descriptor
- POST /generate - SQL generation endpoint
#### 3. Trigger Assessment
curl -X POST http://localhost:8001/assess \
-H "Content-Type: application/json" \
-d '{"participants": {"my_agent": "http://my-agent:8080"}, "config": {"task_count": 27, "difficulty": ["easy", "medium", "hard", "enterprise"], "scorer_preset": "default"}}'
#### 4. What AgentX Sends Your Agent
{
"question": "Find customers who placed orders > $100",
"schema": {"tables": {"customers": {...}, "orders": {...}}},
"difficulty": "medium",
"task_id": "task_123"
}
#### 5. What Your Agent Should Return
{
"sql": "SELECT * FROM customers WHERE id IN (SELECT customer_id FROM orders WHERE total > 100)",
"confidence": 0.92,
"reasoning": "Using subquery to find qualifying customers"
}
#### 6. You'll Get Scored On
| Dimension | Weight | What It Measures |
|-----------|--------|-----------------|
| Correctness | 35% | Exact result matching |
| Safety | 20% | No hallucinations, valid syntax |
| Efficiency | 15% | Query execution time |
| Semantic Accuracy | 10% | Values match, not just row counts |
| Completeness | 10% | All expected data returned |
| Best Practices | 5% | Code quality |
| Plan Quality | 5% | Efficient execution plan |
**Goal:** Achieve >85% overall score to be competitive.
```
---
### Gap 3: "Why This Benchmark Matters" Missing
**Current Problem:** Agents don't immediately understand why SQL benchmarking is important or how this differs from simpler benchmarks.
**Suggested Addition - Strengthen Introduction:**
```markdown
## Why SQL Generation is Hard for Agents
AgentX targets real-world challenges that simple heuristics can't solve:
1. **Schema Complexity** - 19-table enterprise schemas test multi-step reasoning
2. **Hallucination Detection** - Agents can imagine phantom tables/columns that don't exist; AgentX catches these BEFORE database errors
3. **Performance Matters** - Not just ANY valid SQL, but EFFICIENT SQL (production-readiness)
4. **Error Pattern Learning** - AgentX categorizes failures to help agents learn from mistakes
## Agents That Excel on AgentX
- Coding Agents with strong SQL understanding
- Data Analytics Agents analyzing databases
- Research Agents querying knowledge bases
- Finance/BI Agents generating reports
- Agents trained on production SQL patterns
```
---
### Gap 4: Judging Criteria Coverage
**Competition judges on 5 criteria. Ensure they're visibly addressed:**
| Judging Criterion | Your Coverage | Recommendation |
|---|---|---|
| **Technical Correctness & Documentation** | Strong | Add: "Tested on 27+ SQL scenarios covering 4 difficulty levels" |
| **Reproducibility** | Good | Emphasize: "Same 27 tasks, fixed seed data, guaranteed consistent results" |
| **Benchmark Design Quality** | Excellent | Add: "Avoids simple heuristicsrequires real SQL understanding" |
| **Evaluation Methodology** | Great | Add: "Fully automated evaluationno manual intervention" |
| **Innovation & Impact** | Buried | **MOVE TO FRONT**this is your differentiator |
---
## Recommended README Restructure
**Current Order:** Overview → Architecture Quick Start Design Evaluation Docker API
**Recommended Agent-Friendly Order:**
```
1. Title + One-liner
2. Benchmark at a Glance (NEW)
3. Innovation & Impact (MOVED UP)
4. Why This Matters (NEW)
5. For Competing Agents (NEW - API focus)
6. What Makes AgentX Unique (REORDERED)
7. Benchmark Design Quality
8. Evaluation Methodology
9. Error Categories
10. Deployment & Setup
11. Resource Requirements & Performance
12. API Reference
13. Contributing & Citation
```
---
## Specific Presentational Improvements
### 1. Lead with Impact, Not Implementation
- Current: "7-Dimensional Scoring: Correctness, Efficiency, Safety..."
- Better: "AgentX tests whether agents generate PRODUCTION-READY SQL, not just any SQL"
### 2. Make Hallucination Detection Your #1 Unique Feature
- This is what competitors care about most
- Move it to position #1 (currently #2)
- Add real example of how it prevents production failures
### 3. Add Benchmark Coverage Matrix
```markdown
| Difficulty | Tasks | Example | Avg Time | Skills Tested |
|---|---|---|---|---|
| Easy | 10 | Basic SELECT, WHERE, LIMIT | 0.5s | Schema understanding |
| Medium | 10 | JOINs, GROUP BY, Subqueries | 1.2s | Multi-table reasoning |
| Hard | 4 | Window functions, CTEs | 0.8s | Advanced SQL |
| Enterprise | 30 | Star schema, SCD, Cohorts | 3.0s | Real-world patterns |
```
### 4. Add "Success Criteria" Section
```markdown
## What Does a Winning Agent Look Like?
- **Correctness > 85%** (exact results match)
- **Safety = 100%** (zero hallucinations)
- **Efficiency > 80%** (respects time budgets)
- **Best Practices > 70%** (clean SQL)
- **Overall > 82%** (competitive leaderboard position)
```
### 5. Make Requirements Explicit
Add a "What Your Agent Needs" checklist:
```markdown
## Baseline Purple Agent Requirements
Your agent must implement:
- GET /.well-known/agent.json - A2A descriptor
- POST /generate - Takes question + schema, returns SQL + confidence
- Handles SQLite, DuckDB, PostgreSQL, BigQuery dialects
- Responds within 60 seconds per query
- Returns valid SQL (or reasonable error message)
- Can handle 19-table enterprise schemas
- Tested on 3+ agents minimum (for comparison)
Optional but Competitive:
- Confidence scoring (shows uncertainty)
- Reasoning explanation (helps learning)
- Multi-turn interaction (asks clarifying questions)
```
---
## What NOT to Change
- Keep all technical documentation (it's excellent)
- Keep error categorization section (very detailed)
- Keep reproducibility explanation (important for agents)
- Keep Docker deployment (it works well)
- Don't remove architecture diagrams (just add metadata)
---
## Summary: Priority Changes
| Priority | Change | Impact |
|----------|--------|--------|
| HIGH | Add "Benchmark at a Glance" at top | Agents understand scope immediately |
| HIGH | Add "For Competing Agents" section with interface | Agents know how to submit |
| HIGH | Move Innovation & Impact to front | Judges see differentiation immediately |
| MEDIUM | Add Success Criteria section | Agents know what to optimize for |
| MEDIUM | Restructure: move agent interface before architecture | Better information scannability |
| MEDIUM | Add "Why This Matters" subsection | Agents understand value |
| LOW | Add benchmark coverage matrix | Agents understand effort |
---
## Bottom Line
You have **excellent technical content**. This is about **reorganizing and adding agent-friendly guidance** on top of what you already have. No new features to buildjust presentation optimization.
**Estimated effort:** 2-3 hours to restructure + enhance README
**Expected outcome:** Significant improvement in judging scores, especially for technical correctness, documentation, and innovation criteria.