# IETF Draft Analyzer

Track, categorize, rate, and visualize AI/agent-related IETF Internet-Drafts.

**260 drafts** analyzed across **19 categories** with **403 authors**, **1,262 extracted ideas**, and **12 identified gaps** — spanning June 2025 to February 2026.

## What This Does

The IETF is experiencing an unprecedented wave of standardization activity around AI agents. This tool provides a quantitative lens on that activity:

- **Fetches** draft metadata and full text from the IETF Datatracker API
- **Rates** each draft on 5 dimensions (novelty, maturity, overlap, momentum, relevance) using Claude
- **Embeds** drafts with Ollama for pairwise similarity and clustering
- **Extracts** discrete technical ideas and identifies landscape gaps
- **Maps** the author collaboration network and organizational affiliations
- **Generates** interactive visualizations, markdown reports, and a filterable browser
- **Produces** publication-ready figures for an arXiv paper

## Quick Start

```bash
# Install
pip install -e .

# Set your API key (or add to .env file)
export ANTHROPIC_API_KEY=sk-ant-...

# Fetch drafts from IETF Datatracker
ietf fetch

# Rate all unrated drafts with Claude
ietf analyze --all

# Generate embeddings (requires Ollama running locally)
ietf embed

# Extract technical ideas
ietf ideas --all

# Identify gaps in the landscape
ietf gaps

# Generate all visualizations
ietf viz all

# Open the interactive browser
xdg-open data/figures/browser.html
```

## CLI Commands

### Core Pipeline

| Command | Description |
|---------|-------------|
| `ietf fetch` | Fetch AI/agent drafts from IETF Datatracker |
| `ietf analyze --all` | Rate all unrated drafts using Claude (5 dimensions + summary) |
| `ietf embed` | Generate semantic embeddings via Ollama |
| `ietf ideas --all` | Extract technical ideas from drafts using Claude |
| `ietf gaps` | Identify under-addressed areas in the landscape |
| `ietf authors --fetch` | Fetch author/affiliation data from Datatracker |

### Exploration

| Command | Description |
|---------|-------------|
| `ietf list` | List tracked drafts |
| `ietf show <name>` | Show detailed info for a specific draft |
| `ietf search <query>` | Full-text search across all stored drafts |
| `ietf similar <name>` | Find the most similar drafts by embedding similarity |
| `ietf clusters` | Find clusters of near-duplicate drafts |
| `ietf compare <name1> <name2> ...` | Compare drafts for overlap and unique contributions |
| `ietf authors` | Show top authors and their draft counts |
| `ietf network` | Show organizational collaboration network |

### Visualizations (`ietf viz`)

All outputs go to `data/figures/`. Interactive charts are standalone HTML files (no server needed).

| Command | Output | Format |
|---------|--------|--------|
| `ietf viz all` | Generate everything below | mixed |
| `ietf viz browser` | Filterable draft browser with search, category chips, score sliders | HTML |
| `ietf viz landscape` | t-SNE/UMAP 2D scatter of all drafts colored by category | HTML |
| `ietf viz heatmap` | 260x260 clustered pairwise similarity matrix | PNG |
| `ietf viz distributions` | Violin plots for all 5 rating dimensions by category | PNG |
| `ietf viz timeline` | Stacked area chart of monthly submissions by category | HTML |
| `ietf viz bubble` | Novelty vs Maturity explorer (size=relevance, color=category) | HTML |
| `ietf viz radar` | Average rating profile per category | HTML |
| `ietf viz network` | Author co-authorship force-directed graph | HTML |
| `ietf viz treemap` | Category composition treemap (color=avg score) | HTML |
| `ietf viz quality` | Score vs uniqueness with quadrant annotations | HTML |
| `ietf viz orgs` | Organization contribution horizontal bar chart | HTML |
| `ietf viz ideas` | Ideas frequency by type | HTML |

### Reports (`ietf report`)

Markdown reports saved to `data/reports/`.

| Command | Description |
|---------|-------------|
| `ietf report overview` | Sortable table of all rated drafts with bar-chart scores |
| `ietf report landscape` | Category-grouped view with per-category rankings |
| `ietf report timeline` | Monthly submission volume and category trends |
| `ietf report overlap-matrix` | Top similar pairs, per-category overlap, cross-category matrix |
| `ietf report authors` | Top authors, organizations, collaboration pairs |
| `ietf report digest` | Weekly digest of recently fetched drafts |
| `ietf report ideas` | Most common ideas, unique ideas, ideas by type |

### Other

| Command | Description |
|---------|-------------|
| `ietf draft-gen <topic>` | Generate an Internet-Draft addressing a landscape gap |
| `ietf config` | Show or modify configuration |

## Rating System

Each draft is scored 1-5 on five dimensions:

| Dimension | What it measures |
|-----------|-----------------|
| **Novelty** | Originality relative to existing standards |
| **Maturity** | Completeness of specification |
| **Overlap** | Redundancy with other drafts (5 = heavily overlapping) |
| **Momentum** | Community engagement, revisions, adoption |
| **Relevance** | Importance to the AI/agent ecosystem |

**Composite score:**

```
score = 0.30 * novelty + 0.25 * relevance + 0.20 * maturity + 0.15 * momentum + 0.10 * (6 - overlap)
```

## Key Findings

- **36x growth** in 9 months (2 drafts/month to 72)
- **7.9% of draft pairs** exceed 0.80 cosine similarity — significant redundancy
- **Safety deficit**: AI safety proposals (36) are vastly outnumbered by protocol proposals (290+)
- **Organizational concentration**: Top 5 orgs contribute ~35% of all drafts
- **1,262 technical ideas** extracted across 6 types (mechanism, architecture, protocol, pattern, extension, requirement)
- **12 identified gaps** in the current landscape (3 critical, 6 high, 3 medium)

## Gap Analysis

Claude-powered gap analysis identifies 12 under-addressed areas across the 260-draft landscape. Each gap is cross-referenced with the drafts and ideas that partially touch on the topic, highlighting where effort is concentrated and where it's missing.

### Critical Gaps

| # | Gap | Category | Drafts in Category | Key Issue |
|--:|-----|----------|-------------------:|-----------|
| 1 | **Agent Resource Management** | Autonomous netops | 60 | No framework for scheduling, quotas, or fair allocation when agents compete for compute, memory, and bandwidth. Drafts focus on communication but ignore resource contention in multi-agent environments. |
| 2 | **Agent Behavior Verification** | AI safety/alignment | 36 | No runtime mechanisms to verify that deployed agents actually behave according to declared policies. Gap between stated capabilities and observed behavior. Closest work: `draft-birkholz-verifiable-agent-conversations` (attestation), `draft-aylward-daap-v2` (accountability). |
| 3 | **Agent Error Recovery & Rollback** | Autonomous netops | 60 | Missing standards for cascading failure recovery and rollback of autonomous decisions. Only `draft-yue-anima-agent-recovery-networks` specifically addresses recovery; `draft-srijal-agents-policy` touches mandatory failure behavior. |

### High-Severity Gaps

| # | Gap | Category | Drafts in Category | Key Issue |
|--:|-----|----------|-------------------:|-----------|
| 4 | **Cross-Protocol Translation** | A2A protocols | 92 | 92 competing A2A protocol drafts with high overlap but no universal translation layer or negotiation mechanism for interoperability between them. |
| 5 | **Agent Lifecycle Management** | Agent discovery/reg | 57 | Registration and discovery are covered but no standards for agent versioning, updates, graceful shutdown, or retirement without disrupting dependent services. |
| 6 | **Multi-Agent Consensus** | A2A protocols | 92 | No framework for groups of agents to reach consensus on conflicting decisions. Closest: `draft-li-dmsc-inf-architecture` (DMSC protocol), `draft-takagi-srta-trinity` (SRTA architecture). |
| 7 | **Human Override & Intervention** | Human-agent interaction | 22 | Only 22 drafts (vs 60 autonomous netops) address human-agent interaction. No emergency override protocols. Best effort: `draft-irtf-nmrg-llm-nm` (human-in-the-loop framework). |
| 8 | **Cross-Domain Security Boundaries** | Agent identity/auth | 98 | Missing frameworks for agents operating across security domains with different trust levels. `draft-diaconu-agents-authz-info-sharing` and `draft-cui-dmsc-agent-cdi` are early attempts but lack enforcement mechanisms. |
| 9 | **Dynamic Trust & Reputation** | Agent identity/auth | 98 | Static certificate-based auth is insufficient for long-running autonomous systems. No dynamic trust scoring or reputation tracking. Closest: `draft-cosmos-protocol-specification` (trust scoring), `draft-jiang-seat-dynamic-attestation`. |

### Medium-Severity Gaps

| # | Gap | Category | Drafts in Category | Key Issue |
|--:|-----|----------|-------------------:|-----------|
| 10 | **Agent Performance Monitoring** | Autonomous netops | 60 | No standardized metrics, SLOs, or observability framework for production agent deployments. `draft-fu-nmop-agent-communication-framework` mentions monitoring but doesn't define standards. |
| 11 | **Agent Explainability** | AI safety/alignment | 36 | No protocols for agents to explain decisions to other agents or humans. Critical for debugging and regulatory compliance. Only 36 safety drafts total. |
| 12 | **Agent Data Provenance** | Data formats/interop | 102 | No standards for tracking data lineage as information flows between agents. 102 data format drafts but none address provenance tracking. |

### Gap Coverage Ratio

The safety deficit is the most striking finding — only **12.3%** of categorized drafts (36/292) address AI safety/alignment, while 92 focus on A2A protocols and 60 on autonomous operations. The ratio of "how to do things" to "how to do things safely" is roughly **7:1**.

## Tech Stack

- **Python 3.11+** with Click CLI
- **SQLite** with FTS5 full-text search and WAL mode
- **Anthropic Claude** (Sonnet 4) for analysis, rating, idea extraction, gap analysis
- **Ollama** (nomic-embed-text) for local embeddings and similarity
- **Plotly** for interactive HTML visualizations
- **Matplotlib/Seaborn** for publication-ready static figures
- **NetworkX** for author collaboration graph analysis
- **NumPy/SciPy/scikit-learn** for similarity computation and dimensionality reduction

## Project Structure

```
src/ietf_analyzer/
    cli.py          # Click CLI entry point (all commands)
    fetcher.py      # IETF Datatracker API client
    analyzer.py     # Claude-based analysis, rating, idea extraction, gap analysis
    embeddings.py   # Ollama embeddings + cosine similarity + clustering
    db.py           # SQLite with FTS5 (7 tables: drafts, ratings, embeddings, llm_cache, authors, draft_authors, ideas, gaps)
    models.py       # Author, Draft, Rating dataclasses
    reports.py      # Markdown report generation
    visualize.py    # Interactive HTML + static PNG visualizations
    authors.py      # AuthorNetwork: Datatracker author fetching, collaboration graph
    draftgen.py     # Internet-Draft generation from gap analysis
    config.py       # Configuration with defaults

data/
    drafts.db       # SQLite database (all analysis data)
    reports/        # Generated markdown reports
    figures/        # Generated visualizations (HTML + PNG)

paper/
    main.tex        # arXiv paper: "The AI Agent Standardization Wave"
    export_figures.py  # Export interactive charts to static images
    Makefile        # Build: make pdf
```

## Database Schema

| Table | Purpose | Records |
|-------|---------|--------:|
| `drafts` | Draft metadata + full text | 260 |
| `ratings` | 5-dimension AI ratings + summary | 260 |
| `embeddings` | Semantic vectors (nomic-embed-text) | 260 |
| `llm_cache` | Claude API response cache | ~500 |
| `authors` | Person records from Datatracker | 403 |
| `draft_authors` | Author-draft relationships | 742 |
| `ideas` | Extracted technical ideas | 1,262 |
| `gaps` | Gap analysis results | 12 |
| `drafts_fts` | FTS5 full-text search index | — |

## arXiv Paper

A 13-page paper is included in `paper/main.tex`:

> **The AI Agent Standardization Wave: A Quantitative Analysis of 260 IETF Internet-Drafts on Autonomous Agents and Artificial Intelligence**

Build with:

```bash
cd paper
python3 export_figures.py   # copy/export figures
pdflatex main.tex           # compile (run twice for references)
```

## Configuration

```bash
# Show current config
ietf config

# Change Claude model
ietf config --set claude_model claude-sonnet-4-20250514

# API key via .env file (auto-loaded)
echo "ANTHROPIC_API_KEY=sk-ant-..." > .env
```

## Cost

Full analysis of 260 drafts consumed ~475K API tokens (rating + idea extraction + gap analysis). At current Sonnet pricing, this is approximately $2-3 USD.

## License

MIT