Hermes Pipeline
bd457c0b6d
Extracted from: https://github.com/pipeshub-ai/pipeshub-ai.git Score: 1.0
35 lines
2.8 KiB
JSON
35 lines
2.8 KiB
JSON
{
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"name": "agent-builder-workflow",
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"version": "1.0.0",
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"goal": "Build no-code AI agents that connect to enterprise knowledge sources, perform unified search and deep research, and generate explainable answers with citations",
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"inputs": [
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"Task description and agent objectives (e.g., answer Q&A, research specific topics, generate reports)",
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"Knowledge sources (documents, databases, enterprise systems) to connect to",
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"Retrieval strategy configuration (graph-based knowledge graph vs. vector search)",
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"Output requirements (citation format, response structure, code execution needs)"
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],
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"steps": [
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"Step 1: Define agent task and objectives - Specify what the agent should do (e.g., answer a question, perform deep research on a topic, generate a report with citations)",
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"Step 2: Configure knowledge sources - Connect to enterprise documents, databases, or external systems that will serve as the agent's context",
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"Step 3: Build LangGraph chain - Create a workflow chain using LangGraph that combines retrieval (graph/vector) and LLM response generation with citation capabilities",
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"Step 4: Execute agent - Run the LangGraph chain to process the task and generate responses with grounded citations",
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"Step 5: (Optional) Code execution sandbox - If the agent needs to generate or execute code, deploy it to a safe sandbox environment for verification"
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],
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"outputs": [
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"Agent execution logs showing retrieval steps and LLM responses",
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"Grounded answers with block citations to source documents",
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"Generated reports or artifacts (if code execution was performed)",
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"Structured task completion status and results"
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],
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"failure_modes": [
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"Insufficient retrieval results due to poor knowledge graph connectivity or vector embedding quality",
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"Permission errors when accessing enterprise knowledge sources",
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"LLM context window overflow when generating long explanations with citations",
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"Sandbox execution failures for code generation or execution tasks",
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"Timeout errors during multi-step agent chain execution"
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],
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"confidence": 0.95,
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"explanation": "PipesHub provides a reusable agent builder workflow that combines LangGraph orchestration with graph-based and vector-based retrieval. This pattern can be adapted to any enterprise context where AI agents need to search across multiple knowledge sources, generate explainable answers with citations, and optionally execute code in a safe sandbox. The workflow is defined by specific configuration files (LangGraph chain definitions) and follows a standard pattern: task definition \u2192 knowledge source connection \u2192 retrieval strategy \u2192 response generation \u2192 optional code sandbox.",
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"source_repo": "https://github.com/pipeshub-ai/pipeshub-ai.git",
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"score": 1.0
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} |