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Agentic context processor

Introduced 3.3

The agentic_context search response processor adds agent execution context information to search response extensions. This processor works in conjunction with the agentic query translator to expose the agent’s query translation process and maintain conversation continuity:

  1. The processor retrieves agent context information from the pipeline processing context.
  2. Based on the processor configuration, it selectively includes the agent steps summary and the query domain-specific language (DSL) query in the response.
  3. The memory ID is always included, when available, for conversation continuity.
  4. The context information is added to the search response extensions.
  5. Type validation ensures that all context attributes are strings.

This processor works with both conversational and flow agents, but the available context information differs by agent type. Flow agents provide dsl_query (the generated DSL) only, while conversational agents provide dsl_query, memory_id, and agent_steps_summary. For more information, see Agent types.

Request body fields

The following table lists all available request fields.

Field Data type Description
agent_steps_summary Boolean Whether to include the agent’s execution step summary in the response. Available for conversational agents only. Optional. Default is false.
dsl_query Boolean Whether to include the generated DSL query in the response. Available for both conversational and flow agents. Optional. Default is false.

Response fields

When enabled, the processor adds the following fields to the search response extensions.

Field Description
agent_steps_summary A summary of the steps that the agent took to translate the natural language query (included when agent_steps_summary is true). Available for conversational agents only.
memory_id The conversation memory ID for maintaining context across queries. Available for conversational agents only. Only provide this in the agentic query if you want to continue the previous conversation.
dsl_query The generated DSL query that was executed (included when dsl_query is true). Available for both conversational and flow agents.

Example

The following example request creates a search pipeline with an agentic_context response processor:

PUT /_search/pipeline/agentic_pipeline
{
  "request_processors": [
    {
      "agentic_query_translator": {
        "agent_id": "your-agent-id"
      }
    }
  ],
  "response_processors": [
    {
      "agentic_context": {
        "agent_steps_summary": true,
        "dsl_query": true
      }
    }
  ]
}

Perform a search using the configured pipeline:

POST /your-index/_search?search_pipeline=agentic_search_pipeline
{
    "query": {
        "agentic": {
            "query_text": "Show me shoes in white color",
            "memory_id": "your memory id"
        }
    }
}

The response contains the steps taken by the agent to translate the query, the memory ID, and the rewritten DSL query:

{
  "took": 15,
  "hits": {
    "_shards": {...},
    "hits": [...]
  },
   "ext": {
        "agent_steps_summary": "I have these tools available: [ListIndexTool, IndexMappingTool, query_planner_tool]\\nFirst I used: ListIndexTool — input: \"\"; context gained: \"Discovered products-index which seems relevant for products and pricing context\"\\nSecond I used: IndexMappingTool — input: \"products-index\"; context gained: \"Confirmed presence of category and price fields in products-index\"\\nThird I used: query_planner_tool — qpt.question: \"Show me shoes that cost exactly 100 dollars.\"; index_name_provided: \"products-index\"\\nValidation: qpt output is valid and accurately reflects the request for shoes priced at 100 dollars.",
        "memory_id": "WVhHiJkBnqovov2plcDH",
        "dsl_query": "{\"query\":{\"bool\":{\"filter\":[{\"term\":{\"category\":\"shoes\"}},{\"term\":{\"price\":100.0}}]}}}"
    }
}
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