The Hidden Challenges of Scaling AI Agents: Beyond the Fun of Building

Everyone talks about building AI agents like it’s the fun part. But ask anyone running them at scale, and they’ll tell you the real headache is keeping those agents reliable and responsive, especially when you have dozens or hundreds of them interacting.

Why Scaling AI Agents is Hard

Agentic systems are inherently non-deterministic and stateful, operating in a constantly changing environment. This leads to unique challenges in production:

  • Workflow Stalls: Processes can get stuck mid-execution.
  • Memory Loss: Crucial information can disappear during conversations.
  • Data Overload: Systems struggle to handle incoming data floods.

These aren’t simple bugs. They’re classic distributed system issues related to failure recovery, data consistency, scaling, and backpressure. Most AI frameworks lack the tools to address these complexities, resulting in downtime, lost context, and unpredictable behavior.

Akka: Tackling the Hard Problems of Agentic AI

I’ve found that Akka excels at solving these hard problems in agentic AI at scale. Here’s how:

Orchestration

  • Exactly-once Workflows: Ensures reliable execution with durable state, event sourcing, and snapshots.
  • Complex Workflows: Handles multi-agent workflows, human checkpoints, and automatic failure recovery.

Agents

  • Actor Model: Built on the Actor Model, Akka agents manage session memory, retries, and tool calls, operating statefully or statelessly.
  • Distributed Complexity Abstraction: Focus on AI logic, not infrastructure.

Memory

  • Distributed, Sharded, Replicated State: Based on CRDTs and cluster sharding for fast, durable storage with multi-region replication.
  • Context Pruning: Handles token limits gracefully.

Streaming

  • Reactive Streams: Backpressure-aware pipelines for high-throughput data ingestion and routing (audio, video, metrics, LLM tokens) without stalls or drops.

Akka for Python Developers

While Akka is Java-based, it abstracts much of the distributed complexity, making it accessible to Python developers. This allows for building scalable, resilient AI workflows without a steep learning curve.

The Bottom Line

Building a single AI agent is relatively easy. However, scaling agentic systems to meet enterprise demands requires a platform designed for:

  • Distributed Workflows
  • Durable Memory
  • Real-Time Streaming

Akka provides the certainty needed to meet SLAs, enabling a smooth transition from prototype to production with confidence.

Learn more: akka.io