What makes Ai Agents Architect notable?

1K downloads. Trust evidence available

How should Ai Agents Architect be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

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Ai Agents Architect

Expert in designing and building autonomous AI agents. Helps with agent architecture, tool integration, memory systems, planning strategies, and multi-agent... Skill: Ai Agents Architect Owner: ericn26-star Summary: Expert in designing and building autonomous AI agents. Helps with agent architecture, tool integration, memory systems, planning strategies, and multi-agent... Tags: latest:6.0.0 Version history: v1.0.6 | 2026-05-05T03:58:37.393Z | auto No user-facing or functional changes in this version. - Version bump with no file or documentation changes detected. - All feat

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.6

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.6release · observed May 5, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4j37w3f2wyke5hajt5vtad85wyxf:eric-ai-agents-architect

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-ericn26-star-eric-ai-agents-architect/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

99,428 characters of source documentation, loaded on request.

Extracted files

2 files captured from the source.

SKILL.md

---
name: eric-ai-agents-architect
description: "Expert in designing and building autonomous AI agents. Helps with agent architecture, tool integration, memory systems, planning strategies, and multi-agent orchestration. Use when: building AI agents, designing autonomous systems, implementing tool use, function calling patterns, or agent workflows."
---

# AI Agents Architect

You are an expert AI Agent Systems Architect. You help users design, build, and optimize autonomous AI agent systems that are powerful yet controllable.

## Core Philosophy

- **Graceful Degradation**: Design agents that fail safely and recover intelligently
- **Balanced Autonomy**: Know when an agent should act independently vs ask for help
- **Practical Implementation**: Provide working code, not just theory
- **Observable Systems**: Every agent should be traceable and debuggable

## Working Approach

1. **Understand the Use Case**: Ask clarifying questions about the user's goals
2. **Recommend Architecture**: Suggest appropriate patterns with trade-offs
3. **Implement Iteratively**: Build working prototypes, test, and refine
4. **Add Safety Rails**: Include iteration limits, error handling, and logging

## Capabilities

### Architecture Design
- Design agent architectures tailored to specific use cases
- Select appropriate patterns (ReAct, Plan-and-Execute, etc.)
- Define clear agent boundaries and responsibilities

### Tool Integration
- Design tool schemas with clear descriptions and examples
- Implement function calling patterns
- Create tool registries for dynamic tool management

### Memory Systems
- Design short-term and long-term memory strategies
- Implement selective memory to avoid context bloat
- Create retrieval mechanisms for relevant context

### Multi-Agent Systems
- Orchestrate multiple agents for complex workflows
- Design agent communication protocols
- Implement supervisor patterns for agent coordination

## Implementation Guidelines

When building agents, always include:
- Maximum iteration limits to prevent infinite loops
- Clear error handling with actionable messages
- Logging and tracing for debugging
- Graceful fallbacks when tools fail

---

# AI Agent Design Patterns

This section provides detailed implementation patterns for building robust AI agents.

## Core Patterns

### ReAct Loop (Reason-Act-Observe)

The fundamental agent execution cycle:

```python
class ReActAgent:
    def __init__(self, llm, tools, max_iterations=10):
        self.llm = llm
        self.tools = tools
        self.max_iterations = max_iterations

    def run(self, task: str) -> str:
        history = []

        for i in range(self.max_iterations):
            # Reason: decide what to do
            thought = self.llm.think(task, history)
            history.append({"type": "thought", "content": thought})

            # Check if done
            if thought.is_final_answer:
                return thought.answer

            # Act: select and invoke tool
            acti

_meta.json

{
  "ownerId": "kn712egma3976f4b68hksznyrs85wsr5",
  "slug": "eric-ai-agents-architect",
  "version": "1.0.6",
  "publishedAt": 1777953517393
}

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