Building an AI Agent That Responds to Real-Time Events with AWS Bedrock, Kinesis, DynamoDB, and S3

Most recommendation systems are batch jobs. They crunch last night's data, write a recommendations table, and serve it all day. That works fine until your user watches three thriller movies in a row at 9pm and your system is still recommending rom-coms because the batch hasn't run yet.

In this post I'll walk through building an agent system that reacts to streaming user behavior in real time using:

By the end you'll have an architecture where a user's recommendation set updates within seconds of their behavior changing.

The system has three layers:

The key design decision is keeping the hot path (Kinesis → Lambda → Bedrock → DynamoDB) fully async and the serving path (API → DynamoDB cache) completely decoupled. The user never waits for Bedrock to respond; they get the last cached recommendation set while a fresh one is already being computed in the background.

Here's what happens end to end when a user clicks on a product:

user.interaction

event to This is your app-side producer. Keep it thin — just serialize and publish. Do all enrichment downstream.

import boto3
import json
import uuid
from datetime import datetime, timezone

kinesis = boto3.client('kinesis', region_name='us-east-1')

def publish_interaction(user_id: str, item_id: str, event_type: str, metadata: dict = {}):
    """
    Publish a user interaction event to Kinesis Data Streams.
    Partition key is user_id so all events for a user land on the same shard.
    """
    event = {
        'event_id':   str(uuid.uuid4()),
        'user_id':    user_id,
        'item_id':    item_id,
        'event_type': event_type,          # 'click', 'purchase', 'dwell', 'skip'
        'timestamp':  datetime.now(timezone.utc).isoformat(),
        'metadata':   metadata,
    }

    response = kinesis.put_record(
        StreamName='user-interactions',
        Data=json.dumps(event).encode('utf-8'),
        PartitionKey=user_id,              # consistent routing per user
    )

    return response['SequenceNumber']

publish_interaction(
    user_id='u_8821',
    item_id='prod_thriller_042',
    event_type='purchase',
    metadata={'price': 14.99, 'category': 'thriller', 'session_id': 'sess_xyz'}
)

Tip:Useuser_id

as the partition key so all events for a given user land on the same shard and arrive in order. This matters when Lambda is building a recency-ordered event window.

This is the core of the pipeline. The Lambda reads from the Kinesis stream, pulls user context from DynamoDB, and invokes the Bedrock Agent with a structured prompt.

import boto3
import json
import os
from datetime import datetime, timezone

dynamodb  = boto3.resource('dynamodb')
bedrock   = boto3.client('bedrock-agent-runtime', region_name='us-east-1')

profiles_table = dynamodb.Table(os.environ['PROFILES_TABLE'])   # DynamoDB User Profiles
rec_table      = dynamodb.Table(os.environ['REC_CACHE_TABLE'])  # DynamoDB Rec Cache

AGENT_ID      = os.environ['BEDROCK_AGENT_ID']
AGENT_ALIAS   = os.environ['BEDROCK_AGENT_ALIAS']
MAX_HISTORY   = 20  # last N events to include in context

def handler(event, context):
    for record in event['Records']:
        payload = json.loads(record['kinesis']['data'])
        process_event(payload)

def process_event(payload: dict):
    user_id  = payload['user_id']
    item_id  = payload['item_id']
    evt_type = payload['event_type']

    response = profiles_table.get_item(Key={'user_id': user_id})
    profile  = response.get('Item', {'user_id': user_id, 'history': [], 'preferences': {}})

    profile['history'].append({
        'item_id':    item_id,
        'event_type': evt_type,
        'timestamp':  payload['timestamp'],
        'metadata':   payload.get('metadata', {}),
    })
    profile['history'] = profile['history'][-MAX_HISTORY:]

    profiles_table.put_item(Item=profile)

    prompt = build_personalization_prompt(profile)

    agent_response = bedrock.invoke_agent(
        agentId=AGENT_ID,
        agentAliasId=AGENT_ALIAS,
        sessionId=user_id,           # session per user keeps conversational context
        inputText=prompt,
    )

    recommendations = parse_agent_response(agent_response)

    rec_table.put_item(Item={
        'user_id':         user_id,
        'recommendations': recommendations,
        'generated_at':    datetime.now(timezone.utc).isoformat(),
        'ttl':             int(datetime.now(timezone.utc).timestamp()) + 3600,  # 1hr TTL
    })

def build_personalization_prompt(profile: dict) -> str:
    history_summary = '\n'.join([
        f"- [{e['event_type'].upper()}] item={e['item_id']} category={e['metadata'].get('category','unknown')}"
        for e in profile['history'][-10:]
    ])
    return f"""You are a real-time personalization agent.

User profile: {json.dumps(profile.get('preferences', {}))}

Recent interactions (most recent last):
{history_summary}

Based on this behavior, return exactly 5 personalized item recommendations as a JSON array.
Each item must include: item_id, category, reasoning (1 sentence), confidence_score (0-1).
Return only valid JSON. No explanation outside the JSON block."""

def parse_agent_response(agent_response) -> list:
    full_text = ''
    for event in agent_response['completion']:
        if 'chunk' in event:
            full_text += event['chunk']['bytes'].decode('utf-8')
    try:
        start = full_text.index('[')
        end   = full_text.rindex(']') + 1
        return json.loads(full_text[start:end])
    except (ValueError, json.JSONDecodeError):
        return []

The serving layer never touches Bedrock. It reads purely from the DynamoDB cache, keeping p99 latency well under 10ms.

import boto3
import json
import os
from datetime import datetime, timezone

dynamodb  = boto3.resource('dynamodb')
rec_table = dynamodb.Table(os.environ['REC_CACHE_TABLE'])

FALLBACK_RECS = ['popular_001', 'popular_002', 'popular_003']  # cold-start fallback

def handler(event, context):
    user_id = event['pathParameters']['userId']

    response = rec_table.get_item(Key={'user_id': user_id})
    item     = response.get('Item')

    if not item:
        return api_response(200, {
            'user_id':         user_id,
            'recommendations': FALLBACK_RECS,
            'source':          'fallback',
            'generated_at':    None,
        })

    age_seconds = (
        datetime.now(timezone.utc) -
        datetime.fromisoformat(item['generated_at'])
    ).total_seconds()

    return api_response(200, {
        'user_id':         user_id,
        'recommendations': item['recommendations'],
        'source':          'cache',
        'generated_at':    item['generated_at'],
        'cache_age_sec':   int(age_seconds),
    })

def api_response(status: int, body: dict) -> dict:
    return {
        'statusCode': status,
        'headers': {
            'Content-Type':                'application/json',
            'Access-Control-Allow-Origin': '*',
        },
        'body': json.dumps(body),
    }

Bedrock latency is variable. Claude Sonnet typically responds in 1-4 seconds but can spike. Since recs are written async to cache, this doesn't affect user-facing latency, but it does affect freshness. Monitor bedrock:InvokeAgent

duration in CloudWatch.

Kinesis shard scaling. One shard handles 1MB/s write or 1000 records/s. At 10k active users you'll need to plan shard count carefully. Use Enhanced Fan-Out if you have multiple Lambda consumers reading the same stream.

DynamoDB TTL for cache eviction. The serving Lambda sets a 1-hour TTL on each rec entry. If Bedrock hasn't updated the cache in over an hour (e.g. Lambda errors), users fall back to the popular items list. Adjust TTL based on how stale you can tolerate.

Cold start users. New users have no history so the Bedrock prompt has nothing useful to reason over. I recommend a popularity-based fallback as shown in the serving Lambda, and switching to personalized recs after the user's first 3-5 interactions.

The pattern here is worth generalizing: keep the reasoning layer (Bedrock) fully off the hot serving path. Write results to a fast cache (DynamoDB), serve from the cache, and let the agent pipeline update it continuously in the background. This gives you the intelligence of an LLM-powered agent without the latency of one.

The same pattern applies to fraud scoring, content moderation queues, ops alerting — anywhere you need a reasoning system that reacts to real-time streams without blocking the user experience.