AI SummaryGoals is a Rust-based agentic CLI that enables Claude to delegate isolated tasks to subagents via the `invoke_subagent` tool, keeping the main agent's context lean and focused. Developers building multi-step AI workflows benefit from cleaner task decomposition and reliable single-request subagent execution.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to set up the "Goals" agent in my project. Please run this command in my terminal: # Add AGENTS.md to your project root curl --retry 3 --retry-delay 2 --retry-all-errors -o AGENTS.md "https://raw.githubusercontent.com/tallesborges/zdx/master/docs/plans/subagents.md" Then explain what the agent does and how to invoke it.
Description
An agentic CLI, built in rust.
Architecture Overview
All systems use the same fundamental pattern: ` ┌──────────────────────────────────────────────────┐ │ User / Lead Agent │ │ │ │ Decides what to parallelize, creates agents, │ │ assigns tasks, collects results │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ │ │ Agent A │ │ Agent B │ │ Agent C │ │ │ │ (own ctx)│ │ (own ctx)│ │ (own ctx)│ │ │ │ own tools│ │ own tools│ │ own tools│ │ │ └────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ │ │ │ │ └──────────────┴──────────────┘ │ │ Results return to lead │ └──────────────────────────────────────────────────┘ ` Key differences: • Claude Code: Local CLI processes, prompt-only coordination, file-based state, peer-to-peer messaging • Kimi K2.5: Server-side inference sessions, RL-trained parallelization (PARL), up to 100 sub-agents • OpenAI Codex: Thread-based isolation, 4 agent role types, depth limits • Letta Code: CLI process spawning (like zdx), dynamic tool description injection, stream-JSON output • zdx (current): Single Invoke_Subagent tool spawning child zdx exec process, sequential by default, parallel via tokio::JoinSet ---
How It Works
Experimental feature (enabled via CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1) that coordinates multiple Claude Code CLI instances. One session acts as team lead, spawning teammates that work independently in their own context windows. Source: https://github.com/Piebald-AI/claude-code-system-prompts
Goals
• Add a first-class subagent capability so the main agent can delegate isolated tasks without bloating its own context. • Keep subagent execution simple and reliable in MVP (single request, single response, fail-fast). • Learn from real usage what configuration knobs are actually needed before adding them.
Non-goals
• Multi-agent discussion/orchestration loops. • Subagent UI management in TUI (live subagent panels, dashboards). • Persistent subagent sessions/resume semantics. • Automatic summarization/merging of subagent outputs. • Profiles, system_prompt, no_tools — deferred until real usage reveals what's needed.
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