Synchronous communication creates serious reliability issues under heavy enterprise workloads. When business workflows depend on synchronous request-response chains (REST or gRPC) or frequent database polling, latency and tight coupling quickly compound. A single downstream timeout or transient database lock ripples upstream, exhausting connection pools and stalling core operations.
Event-driven architecture addresses this bottleneck. Instead of relying on request-response cycles, applications write state changes to distributed append-only logs and leave execution to autonomous consumers. Rather than waiting for a remote service to finish its work, the publishing service moves on immediately. At Lidex Soluções Digitais, we build distributed event-driven systems that eliminate cascading failures, connect new services with legacy core systems, and preserve transactional integrity under high load.
This guide covers the technical layers required for an enterprise-grade automation backbone: choosing a message engine, propagating transactional state, orchestrating distributed sagas, and running stream-driven AI agents.
How Kafka (KRaft) and Redpanda compare under peak load
An event-driven platform depends directly on its streaming message log. Apache Kafka has long been the default option, though running it historically required ZooKeeper. Kafka 4.0+ eliminated ZooKeeper in favor of native KRaft (Kafka Raft metadata mode), moving cluster orchestration into internal quorum controllers to lower synchronization latency and simplify operations.
Redpanda is an alternative streaming platform compatible with the Kafka protocol, written in C++ using the Seastar framework. It avoids JVM garbage collection pauses by using a thread-per-core architecture that pins partitions and I/O tasks directly to dedicated CPU cores.
When evaluating these two engines, three metrics matter most:
- Publish tail latency (P99): In production benchmarks, Kafka P99 publish latency typically sits between 15 ms and 25 ms, climbing higher when page-cache contention occurs. Redpanda on local NVMe achieves P99 publish latencies between 7 ms and 15 ms, dropping below 1 ms on dedicated bare-metal hardware.
- Disaster recovery and backlog replay: When outages occur, upstream events pile up. In a disaster recovery test replaying an 86-million-event payment backlog across matching cluster setups, Redpanda cleared the backlog 31% faster than a 6-node Kafka cluster.
- Cloud infrastructure spend: Benchmark data from mid-2026 shows that while Redpanda delivers lower tail latency and simpler node setups, AWS Multi-AZ costs are still driven primarily by cross-AZ data transfer and replicated block storage rather than raw broker CPU efficiency.
Decision guide: Keep Kafka in KRaft mode if your architecture depends on the broader ecosystem, including Kafka Connect and standard managed platform setups, or requires high sustained aggregate throughput. Pick Redpanda when sub-15ms tail latency and reduced broker administrative overhead on NVMe storage are your primary requirements.
How the transactional outbox pattern eliminates the dual-write problem
Moving to an event backbone introduces the dual-write problem. When a service updates a business datastore (such as a PostgreSQL order confirmation) and then sends a message to an event broker (such as a Kafka OrderPlaced event), a crash or network drop between the two operations causes data corruption: either the database commits but the event never leaves, or the event publishes while the database transaction rolls back.
Distributed transactions add too much latency and complexity to solve this cleanly. The established pattern is the transactional outbox paired with log-based change data capture (CDC).
[ Application Service ]
│
│ Single ACID Transaction
▼
[ Local Database (e.g., PostgreSQL) ]
├── Business Table (e.g., "orders")
└── Outbox Table (e.g., "outbox_events")
│
│ Database Write-Ahead Log (WAL)
▼
[ Debezium CDC Engine ] ──(Kafka Connect)──► [ Streaming Broker (Kafka/Redpanda) ]
Instead of making a network call to the broker during the user request, the application writes its business record and an event payload into an outbox table within the same local ACID transaction. When the transaction succeeds, both writes commit together.
Relational databases struggle when polling tables directly (SELECT ... FOR UPDATE SKIP LOCKED) at rates above 10,000 writes per second. Modern systems replace polling with log-based CDC through Debezium and Kafka Connect. Debezium reads the database transaction log directly from storage, such as the PostgreSQL write-ahead log (WAL) or MySQL binlog. The Debezium Outbox Event Router then reads those entries and sends them to the appropriate Kafka topics asynchronously without running queries against application tables. This lowers outbox propagation latency below 100 milliseconds without lock contention on business tables.
Because log-based CDC provides at-least-once delivery, consuming services must implement the idempotent consumer pattern. Consumers track deterministic unique identifiers, like UUIDv7 values or composite business keys, in a deduplication store to prevent duplicate processing.
Why pure choreography breaks down in enterprise sagas
When designing multi-stage business workflows, teams pick between choreography and orchestration.
In pure choreography, services publish and consume events without any central coordination. In complex business workflows, this often creates a pinball pattern: business logic ends up scattered across services, handling compensating transactions after failures becomes brittle, and tracking step timeouts becomes difficult.
Distributed event-driven orchestration avoids this fragmentation while keeping services decoupled. Engines like Camunda 8, powered by Zeebe, fit this role well.
Zeebe departs from older relational-backed BPMN engines (like Camunda 7) by running as an append-only, Raft-replicated event stream directly across its cluster nodes. Using partitioned Raft groups instead of centralized SQL row locks allows Zeebe clusters to process tens of thousands of workflow instances per second while managing worker backpressure.
Downstream microservices act as job workers. Instead of receiving synchronous HTTP push requests, workers pull work over gRPC streams and finish tasks asynchronously. Zeebe also supports intermediate event catches, matching inbound events from Kafka or other brokers directly to waiting workflow instances.
Because workflow state is stored in a replicated log, long-running processes survive infrastructure downtime. Worker services can drop offline for hours, reconnect, and pick up right where the committed log left off.
How real-time AI agents operate inside event streams
Between 2023 and 2024, enterprise AI was mostly prompt-driven, built around conversational chatbots and batch vector updates. Real-time enterprise automation requires agents that run continuously, flagging supply chain disruptions, payment fraud, and infrastructure anomalies as soon as an event occurs.
Real-time agentic systems place models directly on the event stream to evaluate incoming data and trigger recovery steps immediately.
[ Raw Event Streams (Kafka / Redpanda) ]
│
▼
[ Stream Processing Engine (RisingWave / Flink SQL) ]
└── Maintains Continuous Materialized Views (Live State)
│
▼ (Model Context Protocol - MCP)
[ Stateful AI Agents (Apache Flink Agents) ]
├── Event-Driven Inference & Decisioning
└── Checkpointed State (RocksDB: Memory / History)
│
▼
[ Autonomous Actions / Orchestration Trigger ]
Running agents on live streams requires solving two main technical problems: maintaining state and keeping context current.
1. In-stream stateful agents (Apache Flink Agents)
Connecting stream engines to machine learning models historically meant building custom wrapper services that polled topics, made REST calls, and tracked conversation state externally.
Apache Flink Agents, added to the Flink ecosystem with v0.1.0 in late 2025 and reaching v0.3.0 in June 2026, runs AI agents as stateful operators inside Flink data streams. Instead of rebuilding context through stateless REST calls on every turn, Flink Agents keeps conversation history, reflections, and tool outputs in Flink's checkpointed RocksDB state. If a node fails, the agent recovers its reasoning state without loss.
2. Streaming materialized views via Model Context Protocol (MCP)
Routing an AI agent directly to an unindexed Kafka topic or running full-table database scans introduces severe latency. Instead, modern systems place a stream processor like RisingWave or Flink SQL between the message broker and the agent to build continuous materialized views from incoming streams.
AI agents query this live state through the Model Context Protocol (MCP) to make decisions. Shifting from polling loops to stream-driven agents cuts response latency by 70% to 90%, allowing models to trigger compensating actions within milliseconds after an upstream event arrives.
An enterprise blueprint for resilient automation
Building an event-driven automation platform requires clear boundaries for asynchronous decoupling, data consistency, and operational governance:
- Decouple ingestion from processing: Synchronous REST and RPC chains fail under heavy enterprise load. Use an event log (Kafka or Redpanda) and log-based CDC (such as Debezium) to decouple services without giving up transactional safety.
- Choose brokers by workload requirements: Pick Redpanda if you need sub-15ms tail latency, low operational overhead, and bare-metal NVMe efficiency. Choose Kafka in KRaft mode if you rely on mature ecosystem tooling like Kafka Connect or standard managed configurations for high sustained throughput.
- Orchestrate complex workflows explicitly: Avoid using pure choreography for multi-step transactional flows. Use event-driven orchestration (such as Zeebe in Camunda 8) to define state transitions, deadlines, and compensations while preserving asynchronous execution.
- Shift from batch AI to streaming intelligence: Run AI agents as stream consumers and tool operators using MCP and stream processing engines, rather than interactive conversational bots. Querying live materialized views gives agents fresh context without the cost of scanning raw event logs.
At Lidex Soluções Digitais, we help companies modernize core workflows and implement reliable event-driven architectures under high transactional loads. Whether you need to update legacy systems, deploy high-throughput event logs, or build real-time data pipelines, our team builds architectures that stay stable at scale.