Why is agentic important?

Agentic AI applications offer tremendous efficiency and productivity gains as humans and devices are augmented with dozens of sleepless autonomous assistants that continuously learn and make decisions. This shift is driving exponential demand for compute and orchestration, because always-on AI assistants become embedded in operations, learning from data and adapting in real time. Companies that can build and scale the infrastructure for these persistent, proactive agents will unlock new levels of cost efficiency and competitive advantage.

 

  Mainframe Web Cloud Mobile Agentic
Users thousands millions 10 millions billions trillions
TPS 100 500 2,500 10,000 1,000,000
  5x 5x 4x 100x

What is an agentic AI app?

Agentic AI applications combine business logic, contextual history, and large language models (LLM) to continuously observe, reason, and act on behalf of users throughout workflows. They don’t just respond to commands — they personalize interactions, automate routine tasks, and adapt to business needs. 

app-ecosystem

Architecturally, agentic AI applications are orchestrated services that maintain conversational context, manage state across interactions, and dynamically adapt to evolving user inputs and environmental interactions. They require seamless coordination between AI models, business rules, data sources, and real-time events — all while preserving long-term memory and intent.

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Key properties of agentic AI apps

Traditional, transaction-centered applications are optimized for short, stateless interactions such as clicking a button or submitting a form. Agentic AI applications are conversation-centered, and need to optimize for long-lived, stateful interactions such as ongoing dialogue or multi-step reasoning.

Conversational

Maintain long-term context and history across interactions.

Autonomous

Operate independently, pursuing goals with minimal human input.

Composeable

Combine agents, functions, and logic into adaptable workflows.

Fault-tolerant and recoverable

Handle errors gracefully, recover state, and guarantee execution even in adverse conditions.

Event-driven

Actions are initiated by external signals.

Streaming

Continuously process a stream of inputs (e.g., video, text, audio).

Akka platform for agentic AI apps

The Akka platform enables developers to build highly efficient agentic AI applications that are elastic, agile, and resilient. 

agentic-AI-API-services

Akka components

  1. The client sends a request to the endpoint component (API).
  2. The endpoint component forwards the command to the App Data Entity, which processes it and emits events.
  3. Events go to a streaming component, which reliably forwards them to Vector DB Service.
  4. Vector DB Service stores and retrieves data for AI processing.
  5. RAG endpoint component retrieves context from Vector DB for AI / LLM.
  6. AI / LLM uses the context to generate an informed response.
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How Akka enables agentic AI applications

Agent lifecycle management

Automatically deploy agents across regions for maximum availability. Elastically scale agents on-demand. Optimize your architecture for maximum efficiency.

  • Agent versioning
  • Agent replay
  • Event, workflow, and agent debugger
  • No downtime agent upgrades
Deployment Model
Multi-region replication
Agent orchestration

Define and manage complex, long-running agents. Integrated support for task coordination, managing state transitions, and durable execution.

  • Event-driven runtime benchmarked to 10M TPS
  • SDK with AI workflow component
  • Serial, parallel, state machine, & human-in-the-loop flows
  • Sub-tasking agents and multi-agent coordination
Workflow
Context database

Efficiently store and retrieve relevant context of infinite length.

  • Agentic sessions with infinite context
  • Context snapshot pruning to avoid LLM token caps
  • In-memory context sharding, load balancing, and traffic routing
  • Multi-region context replication
  • Replication filters for region-pinning user context  data
  • Embedded context per
Event Sourcing
Streaming endpoints

Continuously process video, audio, text and other inputs with unparalleled streaming capabilities.

  • Shared compute: agentic co-execution with API services
  • HTTP and gRPC custom API endpoints
  • Custom protocols, media types, and edge deployments
  • Real-time streaming ingest, benchmarked to over 1TB
Streaming
Agent connectivity and adapters

Reliably connect to large language models, vector databases, and other systems, with automatic backpressure.

  • Non-blocking, streaming  LLM inference adapters with back pressure
  • Multi-LLM selection
  • LLM adapters & 100s of ML algos
  • Agent-to-agent brokerless messaging
  • 100s of 3rd party integrations
Streaming
Developer experience

Akka provides a simple, yet highly expressive, SDK. Write business logic and optimize your AI quality, without worrying about efficiency, scale, or resilience.

Learn more about the SDK

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