Using Agents without Knowledge Base to handle large amount of structured data

To handle large amounts of time-series data from InfluxDB with Amazon Bedrock Agents without using Knowledge Bases, you can implement a custom action group with Lambda functions. Here's how you can approach this:

1. Create a custom action group for your Bedrock Agent that defines operations to retrieve time-series data from InfluxDB based on time period parameters.

2. Implement an AWS Lambda function that:

• Connects to your InfluxDB instance
• Retrieves the requested time-series data based on the time period specified by the user
• Processes and formats the data appropriately for the model
• Returns the formatted data to the agent

3. When a user interacts with your agent and requests information for a specific time period, the agent will invoke the Lambda function through the action group.

4. The Lambda function can implement data reduction techniques to handle the 200K token limit:

• Aggregating data points (e.g., hourly or daily averages instead of per-minute data)
• Filtering to include only relevant metrics
• Summarizing data statistically
• Implementing pagination if needed for extremely large datasets

5. The Lambda function can then format the processed data along with your system prompt in the proper structure for the Bedrock model, such as using the "bedrock-conversation-2024" schema format.

This approach gives you direct control over how the data is retrieved, processed, and sent to the model, allowing you to optimize for the token limits. If the data volume is still too large after reduction techniques, you could implement a strategy where the agent retrieves and processes data in chunks, maintaining context across multiple interactions.

If you find that even with optimization the data exceeds token limits, you might need to consider temporary storage in S3 for intermediate processing, but your Lambda function can be designed to clean up these temporary files after use.
Sources
Bedrock Agent with real time data | AWS re:Post
Option 1: Provide your own prompts for data preparation - Amazon Bedrock
Structured data response with Amazon Bedrock: Prompt Engineering and Tool Use | Artificial Intelligence

Why Agents Aren't Designed for Large Data Payloads

Fundamental Agent Architecture Concerns:

Agents are designed for orchestration, not data transport:

• Agents excel at decision-making and tool invocation
• They're not optimized for data serialization or large payload handling
• Token limits (200K) are intentional constraints for focused reasoning

Performance Issues with Large Payloads:

• Latency: Processing 200K tokens takes 10-30 seconds
• Cost: Large context windows are exponentially expensive
• Accuracy: Model performance degrades with extremely long contexts
• Memory: Large payloads consume significant working memory

You can think of using Agent + InfluxDB MCP Server here.

python
# Agent workflow with InfluxDB MCP server
def time_series_analysis_agent_with_mcp():
    # Step 1: Agent determines query parameters
    query_params = agent.determine_analysis_scope()

    # Step 2: Agent uses InfluxDB MCP server for direct data access
    influx_data = mcp_influxdb.query_time_series(
        bucket="sensors",
        start_time=query_params.start,
        end_time=query_params.end,
        measurement=query_params.metrics,
        aggregate_window="5m"  # Pre-aggregate to reduce data volume
    )

    # Step 3: Agent processes data in intelligent chunks
    results = []
    for time_window in chunk_by_time_windows(influx_data):
        # Process manageable chunks within token limits
        if estimate_tokens(time_window) < 150000:  # Leave buffer
            summary = bedrock.invoke_with_data(time_window, system_prompt)
            results.append(summary)
        else:
            # Further subdivide if needed
            sub_summaries = process_subdivided_chunks(time_window)
            results.extend(sub_summaries)

    # Step 4: Agent synthesizes final response
    return agent.synthesize_results(results)

Benefits of InfluxDB MCP Server Approach:

• Direct Integration: No intermediate storage required
• Real-time Access: Query InfluxDB directly through MCP protocol
• Intelligent Chunking: MCP server can handle optimal data partitioning
• Native Time-Series Operations: Leverage InfluxDB's built-in aggregation functions
• Reduced Latency: Eliminate data export/import steps
• Cost Effective: No S3 storage costs for temporary data

MCP Server Tool Definition:

python
# InfluxDB MCP server tool configuration
influxdb_mcp_tools = {
    "query_aggregated_timeseries": {
        "description": "Query time-series data with automatic aggregation to fit token limits",
        "parameters": {
            "bucket": "InfluxDB bucket name",
            "start_time": "Start time (RFC3339)",
            "end_time": "End time (RFC3339)",
            "measurement": "Measurement name",
            "fields": "List of fields to retrieve",
            "aggregate_window": "Aggregation window (e.g., '5m', '1h')",
            "aggregate_function": "mean|max|min|sum|count"
        }
    },
    "query_chunked_timeseries": {
        "description": "Query large time-series datasets in manageable chunks",
        "parameters": {
            "bucket": "InfluxDB bucket name",
            "time_range": "Overall time range",
            "chunk_duration": "Duration per chunk (e.g., '1h', '6h')",
            "max_tokens_per_chunk": "Maximum tokens per response chunk"
        }
    }
}

Intelligent Data Chunking with MCP:

python
def intelligent_timeseries_chunking():
    # MCP server handles smart chunking based on token limits
    chunks = mcp_influxdb.query_chunked_timeseries(
        bucket="sensors",
        time_range="2024-01-01T00:00:00Z/2024-01-07T23:59:59Z",
        chunk_duration="2h",  # Adjust based on data density
        max_tokens_per_chunk=150000,
        pre_aggregate=True,   # Let InfluxDB aggregate before sending
        aggregate_window="1m"
    )

    analyses = []
    for chunk in chunks:
        # Each chunk is pre-sized to fit within token limits
        analysis = bedrock.analyze_timeseries_chunk(
            data=chunk.data,
            time_window=chunk.time_range,
            system_prompt=system_prompt
        )
        analyses.append(analysis)

    return bedrock.synthesize_temporal_analyses(analyses)

InfluxDB MCP Server Implementation Advantages

Optimized Query Patterns:

flux
// MCP server can execute optimized Flux queries
from(bucket: "sensors")
  |> range(start: -24h)
  |> filter(fn: (r) => r["_measurement"] == "temperature")
  |> aggregateWindow(every: 5m, fn: mean, createEmpty: false)
  |> group(columns: ["location"])
  |> limit(n: 1000)  // Automatic limiting for token management

Token-Aware Data Retrieval:

Automatic Sizing: MCP server estimates token usage before sending data Smart Aggregation: Applies appropriate time-window aggregation Selective Fields: Only retrieves necessary fields to minimize payload Pagination Support: Handles large result sets through pagination

Error Handling and Resilience:

python
# MCP server provides robust error handling
try:
    data = mcp_influxdb.query_with_retry(
        query=flux_query,
        max_retries=3,
        timeout=30,
        fallback_aggregation="1h"  # Increase aggregation if query times out
    )
except InfluxDBTimeoutError:
    # Fallback to more aggressive aggregation
    data = mcp_influxdb.query_aggregated(aggregation_window="1h")

Note: An offline approach that involves exporting data from InfluxDB and Bedrock Batch processing would be better if cost is prefered over real-time operations