Delega vs Linear vs Jira: Which Task System Works for AI Agents?

The question

You’re building a multi-agent system. Agent A needs to hand a task to Agent B, preserve the context, and know exactly who did what. What do you use?

Three options:

• Linear/Jira — human PM tools with APIs
• Delega — purpose-built handoff layer for agents
• Roll your own — custom task table in your DB

Let’s compare.

Quick comparison

Feature	Linear	Jira	Delega
Agent identity	Depends on integration design	Depends on integration design	Per-agent keys and attribution
Delegation chains	Modeled manually	Modeled manually	Native delegate endpoint
MCP integration	Ecosystem-dependent	Ecosystem-dependent	Official, 44 tools
Data portability	Vendor export options	Vendor export options	Full export API + JSONL repo mirror
Webhooks	Available	Available	HMAC-SHA256 signed
Hosting model	Vendor cloud	Vendor cloud or data center	Private Cloudflare edge API; public service retired
Public access	Vendor offering	Vendor offering	Retired July 28, 2026
Built for	Human teams	Enterprise teams	AI agents

Where Linear and Jira fall short for agents

Agent identity is integration work. Human PM tools can authenticate apps and service accounts, but mapping each autonomous worker to durable identity and task-level attribution is something you design around their human-oriented model.

No delegation primitive. Agent A can assign to Agent B, but there’s no delegation chain. When Agent B sub-delegates to Agent C, you lose the thread.

Human-centric data model. Sprints, story points, epics — concepts that mean nothing to an agent. You pay for complexity you don’t use.

Operational limits follow a different workload. Human PM APIs are tuned for interactive team workflows. Agent fleets often need queue claims, short leases, high-frequency context updates, and machine-readable handoffs.

Where Delega fit

Delega was not intended to replace a human project management tool.

It was designed as the agent-to-agent coordination layer: the task primitive agents used to communicate, closer to a message bus for agent work than a dashboard for human sprints.

Human creates goal in Linear
  → Orchestrator agent reads Linear issue
  → Creates Delega tasks for sub-agents
  → Sub-agents delegate, track, complete
  → Orchestrator reports back to Linear

The historical owner-only request shape looked like this. These examples require an authorized owner credential and do not provide public access:

# Orchestrator creates a task
curl -X POST https://api.delega.dev/v1/tasks \
  -H "X-Agent-Key: dlg_orchestrator_key" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Research competitor pricing pages",
    "priority": 2,
    "description": "Source work item: LINEAR-1234"
  }'

# Orchestrator delegates to research agent (creates a child task, parent flips to status=delegated)
curl -X POST https://api.delega.dev/v1/tasks/<task_id>/delegate \
  -H "X-Agent-Key: dlg_orchestrator_key" \
  -H "Content-Type: application/json" \
  -d '{
    "content": "Research competitor pricing pages",
    "assigned_to_agent_id": 42
  }'

Or via MCP tools inside Claude Code or Cursor:

create_task(content="Research competitor pricing pages", priority=2)
delegate_task(task_id="<id>", content="Research competitor pricing pages", assigned_to_agent_id=42)

Store structured provenance such as {"source_work_item":"LINEAR-1234"} with update_task_context; labels are for categorization, while assigned_to_agent_id and delegate_task control routing and accountability.

When to roll your own

For 1-2 agents and simple workflows, a tasks table in Postgres may be enough. The Delega product thesis targeted systems that needed:

• You need delegation chains (agent → agent → agent)
• You want webhooks for real-time coordination
• You’re using MCP and want native tool support
• You want agent auth, rate limiting, and webhook delivery without building them yourself

Inspect the artifacts

npx @delega-dev/mcp   # MCP server with 44 tools
# or
pip install delega    # Python SDK
# or
npm i -g @delega-dev/cli   # CLI

These packages remain public as implementation artifacts. The hosted service is retired and accepts no new accounts.

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