---
title: "Step 7: Self Challenge: DACA Actor Runtime"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Step 7: Self Challenge: DACA Actor Runtime 

Take a deep breath and transform your knowledge into actionable intelligence. In this step, we design and implement the core of our agentic infrastructure: the `BaseActor`, the heart of the **DACA Actor Runtime**. This runtime delivers a plug-and-play foundation for cloud-native AI agents by leveraging Dapr Virtual Actors for scalability, resilience, and event-driven intelligence.

🎯 **End Goal**: A 🔌 Plug-and-Play agent runtime compatible with any agent type, from agent frameworks (OpenAI Agents SDK, LangGraph) to workflows (Temporal, Dapr Workflows).

🔁 **Analogy**: The `BaseActor` is a Dapr Virtual Actor—a fully featured AI agent runtime equipped with:

- Reactive and proactive behaviors
- Event-driven processing   - Event-driven processing
- Direct method handling
- Internal and external system integration
- Robust memory
- Inherent resiliency
- Planning capabilities
- Task scheduling with reminders and timers

### 📌 What We'll Build

- `01_actor_foundation`: Set up the base project
- `02_base_agent_actor`: Define the foundational `BaseActor`

By the end, we’ll have a runtime for agentic applications aligned with the **Dapr Agentic Cloud Ascent (DACA)** pattern, ready to power diverse AI agents.

Actor Model -> Virtual Actor Model -> Dapr Virtual Actors -> Daca Actors

Did you know?
1. You can Dapr SDKs are interacting with Runtime mostly via GRPC. You can write in one language and invoked by actor/service written in another language. -> This means you don't have to care about programming language.
2. Conceptually Actor is doing processing, have storage and communicate. 

## Core Capabilities

We will design to implement following core capabilities, deeply integrated with DACA principles, reflecting a comprehensive feature set learned and refined through the DACA series:

1.  **Reactive & Real-Time Processing:**

    - Handle direct actor invocations gracefully and with low latency (e.g., through a `process_message` method), serving as a primary interaction point for synchronous requests.
    - React swiftly to Dapr actor lifecycle events (`_on_activate`, `_on_deactivate`) for efficient initialization and cleanup.
    - Process incoming data and requests in near real-time, modifying state or triggering further actions, forming the core of the agent's responsive and interactive behavior.

2.  **Proactive & Scheduled Execution:**

    - Utilize Dapr reminders and timers (`Remindable` interface) to schedule and execute tasks autonomously (e.g., periodic health checks, data aggregation, maintenance routines, polling external sources, or triggering self-initiated goals based on internal logic or learned patterns).
    - Allow granular configuration of proactive behaviors, enabling dynamic adjustment of scheduled tasks, frequencies, and parameters.

3.  **Comprehensive Event-Driven Architecture (EDA) Integration:**

    - Integrate seamlessly and robustly with Dapr pub/sub for subscribing to relevant topics and processing events, enabling asynchronous, decoupled communication as advocated by DACA's EDA.
    - Enable actors to react intelligently to a wide array of changes and signals from other agents, microservices, or external event sources (e.g., Kafka, RabbitMQ via Dapr components).
    - Implement sophisticated, idempotent event handling (leveraging unique event IDs and stateful checks) to ensure events are processed reliably and exactly once, critical for system integrity and complex event choreographies.

4.  **Advanced State, Memory, and Knowledge Management:**

    - Provide robust mechanisms for managing diverse forms of agent state and memory: volatile short-term operational state, persistent long-term knowledge, rich conversational context, and belief states using Dapr's state management.
    - Facilitate sophisticated context preparation and management for LLM calls, adhering to principles like 12-Factor Agent's "Own your context window" by selectively loading, summarizing, and vectorizing information as needed.
    - Offer methods for secure state persistence, including hooks for potential encryption/decryption of sensitive data stored in Dapr state stores (e.g., Redis for caching, CockroachDB for relational, Pinecone/Neo4j via intermediary services for vector/graph data).
    - Include strategies for state archival, versioning, and efficient querying where appropriate for long-lived agents.

5.  **Robust Streaming Capabilities:**

    - Provide well-defined interfaces and internal logic for handling incoming data streams chunk by chunk (e.g., `process_data_stream_chunk`), supporting scenarios like large file processing or continuous sensor data ingestion.
    - Include mechanisms for reliable, ordered processing of stream chunks and for finalizing stream processing once all data is received or an end-of-stream marker is detected.
    - Conceptually support initiating and managing outgoing data streams to other actors or services, potentially leveraging Dapr's gRPC proxying capabilities or custom streaming protocols.
    - Ensure stream processing is resilient to interruptions (e.g., through checkpointing partial progress) and supports idempotency for individual chunks to handle retries.

6.  **Dynamic Configuration & Adaptability:**

    - Load actor-specific configurations dynamically during activation from a variety of sources as outlined in DACA, including the Dapr configuration API, environment variables, Kubernetes ConfigMaps, and mounted files.
    - Securely access sensitive configuration data like API keys and tokens using the Dapr secret store building block.
    - Allow secure, dynamic updates to its configuration where feasible, enabling runtime adaptability and behavior modification without redeployment.
    - Store and manage complex configurations that dictate behavior, such as: feature flags, LLM model preferences and parameters, tool schemas for MCP, subscribed event topics, and (in future iterations) Dapr Workflow definitions.

7.  **Intrinsic Security & Authorization:**

    - Provide a flexible authorization framework (e.g., `_authorize_request(caller_info, operation_details, resource_context)`) before executing sensitive operations or accessing specific data, potentially integrating with external policy engines.
    - Facilitate secure interaction patterns, fully leveraging Dapr's security features (mTLS for transport, Dapr secret management API for credentials, scoped API tokens).
    - Emphasize secure coding practices, including comprehensive input validation and sanitization for all external data and API calls.
    - Support audit logging for security-sensitive operations.

8.  **Deep Resiliency, Idempotency & Observability:**

    - Implement robust internal retry mechanisms with configurable backoff strategies for critical operations (e.g., using decorators for retrying calls to external services or state operations), complementing Dapr's built-in retries.
    - Ensure all operations (direct invocations, event handlers, reminder executions, stream chunk processing) are designed for strict idempotency, typically by tracking processed request/event/chunk IDs in persistent state.
    - Gracefully handle errors and exceptions during all phases of the actor lifecycle and processing, with configurable fault tolerance policies.
    - Leverage and extend Dapr's inherent resiliency features (e.g., actor failover, persistent reminders, pub/sub message retries, circuit breakers via Dapr sidecar capabilities).
    - Include comprehensive, structured logging (JSON formatted) for diagnostics, monitoring, and deep observability, aligned with Dapr's OpenTelemetry integration for distributed tracing and metrics collection.

9.  **Integrated Planning & Task Management Capabilities:**
    - Provide internal mechanisms for agents to create, manage, and execute plans or sequences of tasks to achieve complex goals.
    - Allow plans to be stored in actor state, updated dynamically, and tracked for progress.
    - Facilitate the decomposition of high-level goals into smaller, manageable sub-tasks that can be executed sequentially, in parallel, or delegated (potentially to other actors or Dapr Workflows).
    - Include methods for monitoring task status and handling task failures within a plan.

## Key DACA Architectural Alignments

The `BaseActor` is designed to be a cornerstone of the DACA pattern by directly supporting its key architectural tenets:

1.  **Flexible Agent Engine Integration:**

    - Provide a structure that can easily encapsulate or interact with various agentic reasoning engines. While the DACA guide often refers to the OpenAI Agents SDK, this `BaseActor` aims to be adaptable.
    - Specialized actors inheriting from `BaseActor` can integrate specific agent logic from frameworks like OpenAI Agents SDK, LangGraph, or by making direct calls to LLM APIs.
    - The `BaseActor` provides the Dapr-centric capabilities (state, reminders, pub/sub, etc.) that surround the chosen agent engine.

2.  **A2A (Agent-to-Agent) & MCP (Model Context Protocol) Facilitation:**

    - While specific agents will implement the full protocols, `BaseActor` can provide foundational support such as:
      - Helper methods for formatting A2A compliant messages or registering A2A capabilities (Agent Cards).
      - Structures to simplify making calls to MCP servers or exposing actor methods as MCP-compatible tools.
    - This promotes standardized communication and tool usage as envisioned in "Agentia World."

3.  **Human-in-the-Loop (HITL) Integration:**

    - Include standardized methods or event patterns for initiating HITL workflows (e.g., `request_human_review(task_data, confidence_score)`).
    - Provide mechanisms to receive and process human feedback or decisions from HITL systems, allowing the agent to resume or adapt its operation.

4.  **Dapr Workflow Interaction (Phased Approach):**

    - **Current Focus:** The initial implementation will focus on enabling the `BaseActor` itself to be a stateful, long-running entity capable of managing potentially complex internal task sequences and plans (as outlined in "Integrated Planning & Task Management Capabilities").
    - **Future Enhancement:** Offer helper methods to easily initiate and interact with Dapr Workflows. This will be the DACA-preferred way to offload very long-running, multi-step, or complex blocking tasks to external workflow processes, ensuring the actor itself remains responsive. For now, the actor will manage its tasks internally.

5.  **Adherence to 12-Factor Agents Principles:**
    - The design will explicitly aim to enable and simplify adherence to the 12-Factor Agents principles by:
      - Managing prompts and context effectively (Factors 2, 3).
      - Treating tool calls and human interactions as structured data (Factors 1, 4, 7).
      - Unifying execution and business state within the actor model (Factor 5).
      - Exposing control via APIs (Factor 6).
      - Owning control flow (Factor 8, supported by Dapr Workflows).
      - Compacting errors for context (Factor 9).
      - Promoting small, focused agents (Factor 10, as `BaseActor` will be specialized by subclasses).
      - Enabling various trigger mechanisms (Factor 11).
      - Encouraging stateless reducer design for core logic (Factor 12).


## Our Development Approach

We will implement the `BaseActor` in logical phases, treating the completion of each phase as a milestone: 
- **M1: Foundational Structure:** Implement the basic Dapr Actor lifecycle methods (`__init__`, `_on_activate`, `_on_deactivate`), core state management logic, configuration loading mechanisms, and foundational logging. 
- **M2: Core Interactive Capabilities:** Develop and integrate reactive message handling, proactive reminder/timer functionalities, and initial event-driven (pub/sub) integration hooks. 
- **M3: Advanced Agent Features:** Implement advanced state/memory/knowledge management, integrated planning and task management capabilities, conceptual streaming interfaces, and foundational security/authorization mechanisms. 
- **M4: DACA Alignment & Deep Resilience:** Focus on robust DACA architectural alignments (helpers/hooks for OpenAI SDK, A2A, MCP, HITL, Dapr Workflows) and deepen the resiliency, idempotency, and observability features across all capabilities. 
- **M5: Thorough Testing, Documentation & Refinement:** Ensure the `BaseActor` is well-tested (conceptually, with an eye towards future unit/integration tests), comprehensively documented, and refined based on initial implementation insights.
- **M5: BG_HandOff:** Learn how to do background handoffs. Something missing in default handoff sample patterns implementation from LangGraph, CrewAI, OpenAI Agents SDK.

The development process is one of discovery. New requirements, better design patterns, or more effective implementation strategies may emerge. The `BaseActor` design will remain flexible to accommodate such improvements.
