Hey frdel,
I've been reading through Agent Zero's subordinate agent architecture and the call_subordinate delegation model. Impressive work, particularly how cleanly the hierarchical context isolation keeps root-agent reasoning uncluttered.
Opening this as a discussion rather than a feature request, want to gauge whether a concrete pattern I've been working on fits anywhere in your roadmap.
Observation
Agent Zero's subordinate-agent contract is currently implicit. The root agent describes the task in natural language via prompt templates, and the subordinate interprets it. That works well for open-ended delegation, but for multi-step pipelines with structured handoffs I've found it causes a specific failure mode: subordinates produce intent summaries ("I would gather sources, analyze them, synthesize a report...") instead of the actual structured artifacts the root agent then has to consume.
Proposal
A project I maintain, LOGIC.md is a declarative YAML format for specifying agent reasoning contracts. Output schemas, quality gates, halting policies, fallback chains. Framework-agnostic by design. Python SDK on PyPI.
Concrete integration idea for Agent Zero:
yaml# Optional contract the root agent can hand to a subordinate
spec_version: "1.0"
name: research-subordinate
contracts:
outputs:
sources:
type: array
items:
type: object
properties:
title: { type: string }
url: { type: string }
summary: { type: string }
quality_gates:
post_output:
- check: "outputs.sources.length > 3"
action: retry
max_retries: 2
The subordinate agent receives this as part of its prompt framing, and the runtime validates the output before returning to the root agent. Schema drift, partial completions, and "I would do X" responses all become catchable errors instead of silent pipeline degradation.
Why it might fit Agent Zero specifically
Orthogonal, not replacing. Your prompt templates stay. LOGIC.md adds an optional declarative layer above, used only where structured handoffs matter.
Python-native. pip install logic-md gives you parser + validator. I can draft an Agent Zero adapter if there's interest.
Doesn't constrain the open-ended delegation pattern. Contract-less subordinate calls still work exactly as they do today.
MIT licensed, v1.0 stable spec, 325 tests.
What I'm asking
Is this something you'd want in Agent Zero, either as a built-in feature or as a community-contributed integration? Happy to:
Draft a PR with a minimal call_subordinate_contracted variant
Open a spec extension discussion if there are Agent Zero patterns the current LOGIC.md format doesn't cover
Just take "not a fit, here's why" and move on
Either way, genuinely interested in your take on whether declarative contracts earn their keep in a framework that's designed for flexibility first.
Links:
Repo: https://github.com/SingularityAI-Dev/logic-md
Spec: https://github.com/SingularityAI-Dev/logic-md/blob/main/docs/SPEC.md
Python SDK: pip install logic-md
Thanks for the work on Agent Zero — the hierarchical delegation pattern is genuinely one of the cleaner implementations I've seen.
— Rainier
Hey frdel,
I've been reading through Agent Zero's subordinate agent architecture and the call_subordinate delegation model. Impressive work, particularly how cleanly the hierarchical context isolation keeps root-agent reasoning uncluttered.
Opening this as a discussion rather than a feature request, want to gauge whether a concrete pattern I've been working on fits anywhere in your roadmap.
Observation
Agent Zero's subordinate-agent contract is currently implicit. The root agent describes the task in natural language via prompt templates, and the subordinate interprets it. That works well for open-ended delegation, but for multi-step pipelines with structured handoffs I've found it causes a specific failure mode: subordinates produce intent summaries ("I would gather sources, analyze them, synthesize a report...") instead of the actual structured artifacts the root agent then has to consume.
Proposal
A project I maintain, LOGIC.md is a declarative YAML format for specifying agent reasoning contracts. Output schemas, quality gates, halting policies, fallback chains. Framework-agnostic by design. Python SDK on PyPI.
Concrete integration idea for Agent Zero:
yaml# Optional contract the root agent can hand to a subordinate
spec_version: "1.0"
name: research-subordinate
contracts:
outputs:
sources:
type: array
items:
type: object
properties:
title: { type: string }
url: { type: string }
summary: { type: string }
quality_gates:
post_output:
- check: "outputs.sources.length > 3"
action: retry
max_retries: 2
The subordinate agent receives this as part of its prompt framing, and the runtime validates the output before returning to the root agent. Schema drift, partial completions, and "I would do X" responses all become catchable errors instead of silent pipeline degradation.
Why it might fit Agent Zero specifically
Orthogonal, not replacing. Your prompt templates stay. LOGIC.md adds an optional declarative layer above, used only where structured handoffs matter.
Python-native. pip install logic-md gives you parser + validator. I can draft an Agent Zero adapter if there's interest.
Doesn't constrain the open-ended delegation pattern. Contract-less subordinate calls still work exactly as they do today.
MIT licensed, v1.0 stable spec, 325 tests.
What I'm asking
Is this something you'd want in Agent Zero, either as a built-in feature or as a community-contributed integration? Happy to:
Draft a PR with a minimal call_subordinate_contracted variant
Open a spec extension discussion if there are Agent Zero patterns the current LOGIC.md format doesn't cover
Just take "not a fit, here's why" and move on
Either way, genuinely interested in your take on whether declarative contracts earn their keep in a framework that's designed for flexibility first.
Links:
Repo: https://github.com/SingularityAI-Dev/logic-md
Spec: https://github.com/SingularityAI-Dev/logic-md/blob/main/docs/SPEC.md
Python SDK: pip install logic-md
Thanks for the work on Agent Zero — the hierarchical delegation pattern is genuinely one of the cleaner implementations I've seen.
— Rainier