Alternatives to AWS Bedrock AgentCore for Real-Time Data Streaming

AWS Bedrock AgentCore is the default choice for teams building AI agents on AWS. It provides model access, agent orchestration, tool use, memory, and guardrails in a managed package. For teams already deep in the AWS ecosystem, it is the path of least resistance.

But AgentCore has a gap: it is not a data streaming platform. It can orchestrate agents and give them access to tools, but it does not capture database changes, stream events, or maintain real-time data freshness. If your agents need access to current data from production databases, AgentCore alone will not get you there.

This article evaluates the alternatives and complementary platforms for real-time data streaming alongside (or instead of) AgentCore.

What AgentCore Does Well

Before discussing alternatives, it is worth understanding what AgentCore actually provides:

Model access. Direct integration with Bedrock foundation models (Claude, Llama, Titan) with managed inference endpoints.

Agent orchestration. Define agent behavior, manage conversation state, chain multiple agents together, and handle agent lifecycle events.

Tool use. Agents can call external tools (APIs, Lambda functions, databases) as part of their reasoning. AgentCore manages the tool-calling loop.

Memory and context. Managed conversation memory with configurable retention and summarization.

Guardrails. Content filtering, topic restrictions, and response validation to keep agents within acceptable bounds.

Identity and access. IAM integration for controlling which agents can access which resources.

These are genuine capabilities, and building them from scratch takes months of engineering. AgentCore earns its place for the orchestration layer.

Where AgentCore Falls Short

AgentCore’s gap is not in what it does, but in what it assumes about data access.

AgentCore agents access data through tools, typically Lambda functions that query databases, call APIs, or read from S3. This works, but it creates several problems for real-time use cases:

Direct database queries. When an agent needs customer data, the default pattern is: agent calls a tool, tool queries the production database, result comes back. At low scale, this is fine. At agent scale (dozens of agents, each making multiple queries per second), your production database becomes the bottleneck. Query latency increases, connection pools fill up, and production workloads suffer.

Stale data in S3 or warehouses. Some teams avoid the direct query problem by pointing agents at data in S3 or a data warehouse. But this data is typically hours old, refreshed by batch ETL jobs. An agent making decisions based on 6-hour-old data will make wrong decisions when the underlying data changed.

No change awareness. AgentCore has no concept of data changes. It cannot tell an agent “the customer’s order status just changed” without the agent polling for updates. This polling pattern wastes compute and still introduces latency.

No streaming integration. There is no native way to connect a Kafka topic, a CDC stream, or an event bus to an AgentCore agent. All data must come through the tool-calling interface, which adds latency and complexity.

The Alternatives (and Complements)

The platforms below address AgentCore’s data streaming gap. Most of them are complementary to AgentCore rather than replacements.

Streamkap: Managed CDC with Agent Integration

What it is: A managed CDC and streaming platform that captures database changes in real time and delivers them to destinations with sub-second latency. Native MCP support for direct agent data access.

How it works with AgentCore: Streamkap captures changes from your source databases (PostgreSQL, MySQL, MongoDB, DynamoDB) and streams them to a low-latency data store (Redis, Elasticsearch, ClickHouse). AgentCore agents then query this data store instead of hitting production databases. The data is always fresh (sub-second latency from source change to availability) and the production database is protected from agent query load.

Alternatively, agents can access Streamkap data directly through MCP, bypassing the intermediate data store entirely for simple lookups.

Strengths:

• Sub-second CDC latency
• Native MCP support for agent data access
• Fully managed, with no Kafka or Debezium infrastructure
• Works alongside AgentCore as a data layer
• Per-connector pricing

Weaknesses:

• Not an agent orchestration platform (complements AgentCore, does not replace it)
• Smaller connector catalog than some alternatives

Best fit: Teams using AgentCore that need real-time data from databases without hitting production systems directly.

Confluent (Kafka and Confluent Cloud)

What it is: The managed Kafka platform with CDC (via Debezium), stream processing (ksqlDB and Flink), and 200+ connectors.

How it works with AgentCore: Confluent captures database changes via Debezium connectors, streams them through Kafka topics, and delivers them to a queryable data store via sink connectors. AgentCore agents query the data store through Lambda tools.

Strengths:

• Mature ecosystem with broad adoption
• Extensive connector library
• Supports complex event processing beyond CDC
• Strong community and documentation

Weaknesses:

• High operational complexity even on Confluent Cloud
• CDC requires managing Debezium yourself
• No native agent integration or MCP support
• Cost scales with cluster size and throughput
• Setup measured in weeks

Best fit: Teams that already run Kafka and want to extend it for agent data pipelines.

Redpanda

What it is: A Kafka-compatible streaming platform that replaces Kafka’s JVM-based architecture with a C++ implementation. Claims lower latency and simpler operations than Kafka.

How it works with AgentCore: Similar to Confluent. Redpanda serves as the streaming transport layer. You deploy Debezium for CDC, stream through Redpanda topics, and sink to a queryable store for agent access.

Strengths:

• Lower operational overhead than Kafka
• Kafka-compatible API (works with existing Kafka tools and connectors)
• Better resource efficiency (lower CPU and memory per message)
• No JVM tuning required

Weaknesses:

• Still requires managing Debezium for CDC
• Smaller ecosystem than Confluent
• No native agent integration
• Self-hosted or managed (Redpanda Cloud), but less mature managed offering than Confluent Cloud

Best fit: Teams that want Kafka-compatible streaming with less operational overhead, and that are comfortable managing CDC connectors.

Estuary Flow

What it is: A real-time data integration platform that combines CDC with streaming ETL. Captures changes from databases and delivers them to destinations.

How it works with AgentCore: Similar to Streamkap. Estuary captures database changes and delivers them to a data store that AgentCore agents can query.

Strengths:

• Real-time CDC with competitive latency
• Growing connector catalog
• Combines capture and transformation
• Simpler than running Kafka yourself

Weaknesses:

• Smaller ecosystem than Confluent
• Limited stream processing compared to Flink-based platforms
• No native MCP or agent integration

Best fit: Teams looking for managed real-time CDC that is simpler than Confluent but do not need agent-specific features.

Custom Lambda-based Streaming

What it is: A DIY approach using AWS-native services: DynamoDB Streams, Kinesis Data Streams, Lambda triggers, and EventBridge.

How it works with AgentCore: DynamoDB Streams or Kinesis captures events. Lambda functions process and route them. EventBridge handles event routing. Data lands in a queryable store for agent access.

Strengths:

• Fully AWS-native, no external vendors
• Fine-grained IAM control
• Pay-per-use pricing for low-volume workloads
• Tight integration with other AWS services

Weaknesses:

• Significant custom engineering required
• DynamoDB Streams only works with DynamoDB (not PostgreSQL, MySQL, etc.)
• Kinesis has higher latency than dedicated CDC platforms
• No built-in CDC for relational databases
• Maintenance burden grows with complexity
• Lambda cold starts add latency

Best fit: Teams with small-scale, DynamoDB-only workloads that want to avoid external vendors entirely.

Architecture Patterns

Pattern 1: AgentCore + Streamkap (Recommended)

Source DBs → Streamkap CDC → Agent Data Store → AgentCore Agents
                                    ↑
                              (MCP also available)

This is the simplest and most effective architecture for most teams. Streamkap handles all data streaming concerns. AgentCore handles all agent orchestration concerns. Each platform does what it is best at.

Data freshness: sub-second. Setup time: under an hour. Operational burden: minimal.

Pattern 2: AgentCore + Confluent

Source DBs → Debezium → Kafka → Sink Connectors → Data Store → Lambda → AgentCore Agents

More complex but appropriate for teams that already run Kafka. The additional hops add latency and operational surface area, but you get Confluent’s broad ecosystem.

Data freshness: 1 to 5 seconds. Setup time: weeks. Operational burden: significant.

Pattern 3: AgentCore + AWS Native

DynamoDB → DynamoDB Streams → Lambda → ElastiCache → Lambda → AgentCore Agents

Only viable for DynamoDB-centric workloads. Does not support CDC from relational databases without adding DMS (which has its own limitations).

Data freshness: 1 to 10 seconds. Setup time: days. Operational burden: moderate.

The Key Insight: Orchestration and Streaming Are Separate Problems

The most common mistake teams make is looking for one platform that handles both agent orchestration and data streaming. No platform does both well today.

AgentCore is excellent at orchestration: managing agents, providing model access, handling tool use, enforcing guardrails. But it has no data streaming capabilities.

Streaming platforms (Streamkap, Confluent, Redpanda) are excellent at data movement: capturing changes, streaming events, delivering data with low latency. But they do not orchestrate agents.

The right architecture uses both. AgentCore for the agent layer. A streaming platform for the data layer. Connected through a queryable data store or MCP.

Trying to force AgentCore to be a data platform (by querying production databases through tools) or trying to force a streaming platform to be an agent platform (by building orchestration on top of Kafka) leads to brittle, underperforming systems.

Making the Decision

If you are on AWS and building agents with Bedrock, here is the decision tree:

Do your agents need real-time data from databases?

• No: AgentCore alone is fine. Use Lambda tools to query databases or S3.
• Yes: You need a streaming layer.

Do you already run Kafka?

• Yes: Extend Confluent or Redpanda for agent data delivery.
• No: Use Streamkap for managed CDC without the Kafka overhead.

Do your agents need to be notified of data changes, or do they just need fresh data when they query?

• Fresh data on query: Stream CDC to a low-latency store, have agents query it.
• Proactive notification: Use MCP with Streamkap, or build a push notification layer on top of your streaming platform.

How much operational overhead can your team absorb?

• Minimal: Streamkap (managed everything).
• Some: Estuary or Redpanda Cloud.
• Significant: Confluent (you get maximum control, but you pay for it in engineering time).

The streaming layer is not optional for production AI agent systems. The question is which streaming layer fits your team, your existing infrastructure, and your operational capacity. Pick one and get your agents access to real-time data. They will make better decisions for it.

---

Ready to give your AI agents access to real-time data? Streamkap captures database changes with sub-second latency and delivers them to the data stores your agents query, with native MCP support. Start a free trial or learn more about AI/ML pipelines.