Jomarchy Agent Tools - Architecture

Technical architecture documentation explaining design decisions, schema design, token savings analysis, and tool composition patterns.

Design Philosophy
Why Bash + SQLite (Not MCP)
System Architecture
Database Schema
Tool Composition Patterns
Token Savings Analysis
Performance Characteristics
Security Model

Design Philosophy

Core Principles

Bash composability over HTTP servers
- Tools are standalone executables
- Compose via pipes, redirects, xargs
- No ports, no daemons, no API calls
SQLite for persistent state
- Single-file database (~/.agent-mail.db)
- ACID transactions
- Zero configuration
- Git-friendly (via JSONL backups)
Prompts are code (inspired by Mario Zechner)
- Slash commands are markdown files with instructions
- Claude executes step-by-step logic
- Self-documenting coordination protocols
Minimal token overhead
- Tools return structured output (JSON or text)
- No schema introspection overhead
- 80x token reduction vs MCP servers
Universal compatibility
- Works with any AI assistant that runs bash
- No vendor lock-in
- No MCP dependency

Why Bash + SQLite (Not MCP)

The MCP Problem

Model Context Protocol (MCP) solves coordination, but has significant overhead:

Issue	Impact
Schema introspection	30,000+ tokens per tool connection
Server startup	2-5 seconds per invocation
HTTP overhead	JSON-RPC for every operation
Vendor lock	Only works with MCP-compatible assistants
Complexity	Requires running server processes

The Bash Solution

Jomarchy Agent Tools uses bash scripts + SQLite instead:

Benefit	Value
Minimal tokens	~400 tokens (tool name + --help only)
Instant startup	<10ms per tool
Native pipes	Compose with `jq`, `grep`, `xargs`
Universal	Works with ANY bash-capable assistant
Zero setup	No servers to manage

Result: 80x token reduction with universal compatibility.

Token Cost Comparison

MCP Server (Agent Mail example):

Schema introspection:        32,000 tokens
Per-call overhead:              500 tokens
Tool response:                  200 tokens
──────────────────────────────────────────
Total per operation:         32,700 tokens

Bash Tool (jt example):

Tool name + description:        100 tokens
Help text (on demand):          300 tokens
Tool response:                  200 tokens
──────────────────────────────────────────
Total per operation:            400 tokens  (82x reduction!)

When MCP Makes Sense

MCP is better when:

You need type safety (TypeScript schemas enforced at runtime)
You require IDE integration (autocomplete, type checking)
You want managed connections (connection pooling, retry logic)
You're building cross-platform GUIs (need server abstraction)

Bash tools are better when:

You need minimal overhead (CLI-first workflows)
You want maximum composability (pipe-based data flow)
You require universal compatibility (works everywhere)
You prefer simple maintenance (no servers to manage)

JAT chooses bash for CLI-first AI coding workflows.

System Architecture

High-Level View

┌─────────────────────────────────────────────┐
│  AI Coding Assistants                       │
│  (Claude Code, Cline, Cursor, etc.)         │
└────────────┬────────────────────────────────┘
             │ Uses slash commands
             │ Reads ~/.claude/CLAUDE.md
             ▼
┌─────────────────────────────────────────────┐
│  Coordination Commands (10 .md files)       │
│  ~/.claude/commands/jat/                  │
│  /register, /start, /complete, /handoff,    │
│  /pause, /block, /stop, /status, /verify    │
└────────┬──────────────────┬─────────────────┘
         │                  │
         │ Executes via...  │
         ▼                  ▼
┌──────────────┐   ┌──────────────────────────┐
│ Bash Tools   │   │  State Management        │
│ (28+ tools)  │   │                          │
│              │   │  • Agent Registry (SQLite)│
│ am-*         │◄──┤  • JAT Tasks (per-project)│
│ browser-*    │   │  • File Declarations     │
│ db-*         │   │                          │
└──────────────┘   └──────────────────────────┘

Component Interaction

Workflow Example: /start command

User invokes: /start
Claude reads: ~/.claude/commands/jat/start.md
Expands to steps:
- Parse parameters and mode (quick/normal)
- Detect task type (bulk vs normal)
- Contextualize from conversation OR auto-select
- Check conflicts (git status, dependencies)
- Declare files via jt update --files
- Query dependencies via jt show
- BEGIN WORK
Bash tools execute:
- jt update jat-123 --files "src/**" → SQLite UPDATE
- jt show jat-123 --json → Reads .jat/tasks.db
State persists:
- Agent Registry: ~/.agent-mail.db
- JAT Tasks: .jat/tasks.db (SQLite, per-project)
- File declarations: stored on the task record

Zero HTTP calls. Zero servers. Pure bash + SQLite.

Database Schema

Agent Mail Schema (`~/.agent-mail.db`)

Projects Table

CREATE TABLE projects (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    slug TEXT NOT NULL UNIQUE,           -- URL-safe identifier (e.g., "acme")
    human_key TEXT NOT NULL,             -- Absolute path (e.g., "/home/user/code/acme")
    created_at TEXT NOT NULL DEFAULT (datetime('now'))
);

Purpose: Track git repositories or logical project groupings.

Why slugs? URL-safe identifiers for cross-project references.

Agents Table

CREATE TABLE agents (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    project_id INTEGER NOT NULL,
    name TEXT NOT NULL,                  -- "BlueLake", "GreenCastle" (Adjective+Noun)
    program TEXT NOT NULL,               -- "claude-code", "cursor", "aider"
    model TEXT NOT NULL,                 -- "sonnet-4.5", "gpt-4"
    task_description TEXT DEFAULT '',
    inception_ts TEXT NOT NULL DEFAULT (datetime('now')),
    last_active_ts TEXT NOT NULL DEFAULT (datetime('now')),
    FOREIGN KEY (project_id) REFERENCES projects(id),
    UNIQUE (project_id, name)
);

Purpose: Track agent identities within projects.

Why Adjective+Noun naming? (e.g., "BlueLake", "GreenCastle")

Human-readable
Auto-generated (no naming conflicts)
Easy to reference in messages

Design decision: One agent name per project allows resuming sessions.

Messages Table

CREATE TABLE messages (
    id INTEGER PRIMARY KEY AUTOINCREMENT,
    project_id INTEGER NOT NULL,
    sender_id INTEGER NOT NULL,
    thread_id TEXT,                      -- Use JAT task ID (e.g., "jat-123")
    subject TEXT NOT NULL,
    body_md TEXT NOT NULL,               -- Markdown content
    importance TEXT NOT NULL DEFAULT 'normal', -- normal | high | urgent
    ack_required INTEGER DEFAULT 0,      -- Boolean
    created_ts TEXT NOT NULL DEFAULT (datetime('now')),
    FOREIGN KEY (project_id) REFERENCES projects(id),
    FOREIGN KEY (sender_id) REFERENCES agents(id)
);

Purpose: Async communication between agents.

Why thread_id? Links messages to JAT tasks for traceability.

Why Markdown? Structured content (code blocks, lists, links) in messages.

Message Recipients Table (many-to-many)

CREATE TABLE message_recipients (
    message_id INTEGER NOT NULL,
    recipient_id INTEGER NOT NULL,
    ack_ts TEXT,                         -- Acknowledgment timestamp
    PRIMARY KEY (message_id, recipient_id),
    FOREIGN KEY (message_id) REFERENCES messages(id),
    FOREIGN KEY (recipient_id) REFERENCES agents(id)
);

Purpose: Track who received a message and acknowledgment status.

Why separate table? One message can have multiple recipients.

Indexes for Performance

CREATE INDEX idx_messages_thread ON messages(thread_id);
CREATE INDEX idx_messages_project ON messages(project_id);
CREATE INDEX idx_messages_created ON messages(created_ts);

Note: File declarations are stored on the task record itself (via jt update --files), not in a separate table. They are automatically cleared when the task is closed.

JAT Tasks Schema (`.jat/tasks.db`)

Format: SQLite database (per-project)

Example task:

{
  "id": "acme-abc",
  "title": "Implement OAuth login",
  "description": "Add Google OAuth to auth system",
  "status": "open",
  "priority": 1,
  "issue_type": "feature",
  "created_at": "2025-01-15T10:30:00Z",
  "updated_at": "2025-01-15T10:30:00Z",
  "labels": ["auth", "oauth", "p1"],
  "depends_on": ["acme-xyz"]
}

Why SQLite?

ACID transactions (reliable concurrent access)
Fast queries with indexes
Zero configuration
Per-project isolation

Why per-project databases?

Clean separation (each repo has own tasks)
No global state leakage
IDE aggregation (JAT IDE reads all ~/code/*/.jat/)

Tool Composition Patterns

Pattern 1: Pipes (Data Transformation)

# Get ready tasks as JSON, filter by priority, extract IDs
jt ready --json | \
  jq '.[] | select(.priority <= 1)' | \
  jq -r '.id'

Tools used: jt → jq

Pattern: Transform → Filter → Extract

Pattern 2: Bulk Operations (xargs)

# Close all tasks matching a label
jt list --json | jq -r '.[] | select(.labels | contains(["cleanup"])) | .id' | \
  xargs -I {} jt close {} --reason "Batch cleanup"

Tools used: jt → jq → xargs → jt

Pattern: Query → Extract → Iterate

Pattern 3: Conditional Logic (if/else)

# Check if task has file declarations before starting
task_files=$(jt show jat-abc --json | jq -r '.[0].reserved_files // ""')
if [[ -n "$task_files" ]]; then
  echo "Task already has file declarations: $task_files"
else
  jt update jat-abc --files "src/auth/**"
fi

Tools used: jt → jq → conditional

Pattern: Query → Check → Act

Pattern 4: Parallel Queries (background jobs)

# Gather metrics in parallel
{
  am-agents --json > /tmp/agents.json &
  jt ready --json > /tmp/tasks.json &
  wait
}

# Process results
jq '.[] | .name' /tmp/agents.json

Tools used: Multiple tools in parallel via & and wait

Pattern: Fork → Wait → Process

Pattern 5: Incremental Processing (while read)

# Process each task from JAT Tasks queue
jt ready --json | jq -c '.[]' | while read task; do
  task_id=$(echo "$task" | jq -r '.id')
  priority=$(echo "$task" | jq -r '.priority')
  echo "Processing $task_id (P$priority)"
  # ... do work ...
done

Tools used: jt → jq → while read

Pattern: Stream → Parse → Iterate

Pattern 6: Error Handling (pipefail)

set -euo pipefail

# Safely chain tools (fails if any step fails)
jt ready --json | \
  jq '.[] | select(.priority <= 1)' | \
  tee /tmp/urgent.json | \
  jq -r '.id'

Pattern: Safe chaining with set -euo pipefail

Why? Catches failures in pipe chains.

Token Savings Analysis

Scenario: Agent picks task and starts work

MCP Approach (Agent Mail Server)

1. Connect to MCP server:
   - Schema introspection:           30,000 tokens
   - Tool listing:                    2,000 tokens

2. Get inbox (mcp.agent_mail.get_inbox):
   - Request overhead:                  500 tokens
   - Response JSON:                     800 tokens

3. Get tasks (hypothetical mcp.jat_tasks.get_ready):
   - Request overhead:                  500 tokens
   - Response JSON:                   1,200 tokens

4. Reserve files (mcp.agent_mail.reserve_files):
   - Request overhead:                  500 tokens
   - Response JSON:                     300 tokens

5. Send announcement (mcp.agent_mail.send_message):
   - Request overhead:                  500 tokens
   - Response JSON:                     200 tokens

──────────────────────────────────────────────────
Total:                                36,500 tokens

Bash Approach (JAT)

1. Get tasks (jt ready --json):
   - Tool name:                          20 tokens
   - Response JSON:                   1,200 tokens

2. Start task (jt update --status in_progress --files "src/**"):
   - Tool name + args:                   50 tokens
   - Response text:                     100 tokens

──────────────────────────────────────────────────
Total:                                 1,370 tokens

Savings: 34,110 tokens (93% reduction!)

Scaling Analysis

For 10 agent coordination cycles:

Approach	Tokens per Cycle	Total for 10 Cycles
MCP	36,500	365,000
Bash	2,390	23,900
Savings	34,110	341,100 (93%)

Context window impact:

Claude Sonnet 4.5: 200k context window
MCP approach: 365k tokens = exceeds window (requires summarization)
Bash approach: 23.9k tokens = fits easily with room for code

JAT enables swarm coordination that fits in context windows.

Performance Characteristics

Tool Invocation Latency

Bash tool overhead:

Tool startup:               5-10ms
SQLite query:              10-20ms
JSON parsing (jq):          5-10ms
──────────────────────────────────
Total per invocation:      20-40ms

MCP server overhead:

Server startup:           2,000-5,000ms
HTTP connection:              50-100ms
JSON-RPC overhead:            20-50ms
Schema validation:            10-30ms
──────────────────────────────────────
Total per invocation:    2,080-5,180ms

Result: Bash tools are 52-129x faster (20ms vs 2,080ms).

Database Performance

SQLite characteristics:

Read latency: 1-10ms (indexed queries)
Write latency: 5-20ms (single transaction)
Concurrent reads: Unlimited
Concurrent writes: Serialized (lock-free read-only mode)

Why SQLite is perfect here:

Local file (no network latency)
ACID transactions (no data loss)
Zero configuration (no server setup)
Single-file portability (backup with cp)

Bottleneck: Write-heavy workloads (>100 writes/sec) may see lock contention.

Mitigation: WAL mode enabled (PRAGMA journal_mode=WAL) for better concurrency.

JAT Tasks SQLite Performance

Read performance:

Indexed queries: 1-10ms
Full table scan (<1000 tasks): 5-10ms

Write performance:

Single transaction: 5-20ms
Batch operations: Serialized via WAL mode

Why SQLite for tasks: Fast queries, ACID transactions, and zero configuration. Task updates are infrequent (seconds apart, not milliseconds).

Security Model

Threat Model

Assumptions:

Agents run on trusted local machine (not remote)
Users have file system access (can modify ~/.agent-mail.db)
No hostile agents (agents cooperate, don't sabotage)

Out of scope:

Remote agents (untrusted network)
Malicious agents (code execution attacks)
Multi-user systems (permission isolation)

Security Measures

1. File Declarations (Advisory)

Not enforced. Agents declare which files they plan to edit via jt update --files. Other agents check these declarations voluntarily.

Attack: Malicious agent ignores declarations → writes anyway

Mitigation: Git conflict resolution (manual merge required)

Design choice: Advisory declarations = simpler, sufficient for cooperative agents.

2. SQLite Injection Prevention

Parameterized queries:

# Safe (uses SQLite parameters)
sqlite3 ~/.agent-mail.db \
  "SELECT * FROM messages WHERE thread_id = ?" "$thread_id"

# Unsafe (vulnerable to injection)
sqlite3 ~/.agent-mail.db \
  "SELECT * FROM messages WHERE thread_id = '$thread_id'"

All JAT tools use parameterized queries via am-lib.sh:query_db().

3. Path Validation

File declaration patterns are relative to project root:

# Valid patterns
jt update task-id --files "src/**/*.ts"   # Glob pattern
jt update task-id --files "src/auth/"     # Directory

Validation logic: Patterns must be relative to project root.

4. Agent Name Validation

Enforced format: [A-Z][a-z]+[A-Z][a-z]+ (e.g., "BlueLake")

Why? Prevents injection via special characters in SQL/bash contexts.

5. Database Permissions

File permissions:

~/.agent-mail.db: 0644 (rw-r--r--)
  - Owner: Full read/write
  - Others: Read-only

Why read-only for others? Agents from other users can inspect but not modify.

Best Practices

Run on trusted machines (your laptop, your servers)
Don't expose SQLite database over network
Audit agent behavior (check git history for rogue commits)
Use file declarations (prevent accidental conflicts, not attacks)
Review JAT tasks (verify task descriptions before accepting)

JAT prioritizes simplicity over paranoia. Designed for cooperative agents on trusted machines.

Scalability Considerations

Agent Count

How many agents can coordinate?

SQLite write limit: ~1000 writes/sec (WAL mode)
Typical agent: 1-5 writes/min (messages, reservations)
Theoretical limit: 12,000-60,000 agents

Practical limit: 10-50 agents

Bottleneck: Human coordination (not technical)

Project Count

How many projects can one agent work across?

Agent Mail: One ~/.agent-mail.db for all projects
JAT Tasks: One .jat/ per project
Limit: Unlimited (tested with 50+ projects)

IDE aggregation:

JAT IDE scans ~/code/*/.jat/
Linear scan: ~10ms per project
50 projects: 500ms total

Performance: Sub-second even with 100+ projects.

Message Volume

Archive size growth:

Average message: ~500 bytes
1000 messages/day: ~500 KB/day
1 year: ~180 MB

Cleanup strategy:

Periodic archival (move old messages to .archive table)
Retention policy (delete messages >90 days)

Current implementation: No automatic cleanup (manual if needed).

Future Considerations

Potential Enhancements

Distributed Agent Mail
- Sync ~/.agent-mail.db across machines
- Conflict-free replicated data types (CRDTs)
- Enables remote agent coordination
Advanced File Locking
- Line-level locks (not just file-level)
- Read locks vs write locks (allow concurrent reads)
Agent Swarm Intelligence
- Auto-assign tasks based on agent capabilities
- Load balancing (distribute work evenly)
- Failure recovery (reassign if agent crashes)
Real-Time IDE
- WebSocket updates (no page refresh)
- Agent presence indicators (who's online)
- Live task progress tracking
MCP Compatibility Layer
- Expose JAT tools via MCP protocol
- Best of both worlds (bash tools + MCP integration)

Design Trade-offs

Decision	Benefit	Cost
Bash over HTTP	Instant startup, composability	No remote access
SQLite over PostgreSQL	Zero config, single-file	Write concurrency limit
SQLite per-project (JAT Tasks)	Fast queries, ACID transactions	Not directly git-diffable
Advisory locks	Simple, no enforcement overhead	Requires cooperative agents
Local-only	Fast, no network latency	Can't coordinate across machines

JAT optimizes for:

Local-first development
Minimal overhead
Maximum composability
Universal compatibility

JAT sacrifices:

Remote coordination (without syncing)
Write-heavy workloads (>100/sec)
Enforced isolation (assumes cooperation)

Conclusion

Jomarchy Agent Tools proves that bash + SQLite can power sophisticated multi-agent coordination with 80x token reduction vs MCP servers.

Key architectural insights:

Bash composability > HTTP abstraction for CLI workflows
SQLite provides ACID guarantees without server complexity
Prompts are code enables self-documenting coordination
Advisory locks suffice for cooperative agents
Local-first architecture prioritizes speed and simplicity

When to use JAT:

AI-assisted development workflows
CLI-first coding environments
Token-constrained models
Local development (laptop, workstation)

When NOT to use JAT:

Remote agent coordination (across networks)
High-security environments (need enforced isolation)
Write-heavy workloads (>100 writes/sec)
Multi-user shared systems

JAT's niche: Local-first AI coding with minimal overhead.

For questions or discussions, see CONTRIBUTING.md or open an issue.

Built with:

Bash
SQLite
jq
Git
Philosophy from "Prompts are code" by Mario Zechner

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