# The Queen Node: Architectural Foundations for Centralized Governance in Agentic Swarms via Real-Time Data Fusion

## 1. Introduction: The Governance Crisis in Autonomous Systems

The rapid evolution of Large Language Models (LLMs) from passive text generators to autonomous agents has precipitated a fundamental shift in software architecture. We are transitioning from deterministic, monolithic applications to non-deterministic, distributed multi-agent systems—often termed "swarms." In these environments, individual "worker" agents utilize tools, execute code, and make decisions to achieve high-level goals. However, this autonomy introduces a critical governance crisis. Unlike traditional microservices, which adhere to strict API contracts, AI agents operate probabilistically. They are prone to "hallucination," "semantic drift" (gradual deviation from the original intent), and "misalignment" (executing actions that are technically correct but practically disastrous).

To mitigate these risks, a new architectural primitive is required: the **Centralized Governance Agent**, or the **"Queen"** node. This entity does not perform the work; rather, it observes, judges, and corrects the swarm. The Queen's efficacy depends entirely on her ability to fuse three distinct, asynchronous data streams into a coherent worldview:

- **User Intent**: The immutable, high-level directive provided by the human operator.
- **Worker Telemetry**: The high-velocity, semi-structured exhaust of agent reasoning and tool usage.
- **Ground Truth**: The "Honey Store"—the verifiable state of the world residing in databases, ERPs, and knowledge bases.

This report presents a comprehensive architectural analysis for constructing such a Queen node. It moves beyond simple "orchestration" patterns to propose a **Reactive Governance Architecture**. By leveraging advanced stream processing engines like Pathway and Bytewax, adopting "Online LLM-as-a-Judge" paradigms for stream reasoning, and standardizing communication via a novel "IMJDF" (Intent, Metric, Judgment, Decision, Feedback) protocol over CloudEvents, we can mathematically bound the non-determinism of agentic swarms. This document serves as a technical blueprint for architects seeking to deploy resilient, enterprise-grade agent networks.

---

## 2. Theoretical Framework: The Tri-Stream Fusion Problem

The fundamental challenge in governing autonomous agents is the temporal and semantic misalignment of data. In a traditional control loop, the setpoint (goal), process variable (current state), and control output are all numerical and synchronous. In agentic systems, these variables are semantic (textual/vectorized) and asynchronous.

### 2.1 Stream 1: Original User Intent (The Invariant)

The "User Intent" is the constitutional document of the agent's session. It acts as the invariant against which all actions must be measured.

- **Characteristics**: Low velocity (often static per session), high semantic density.
- **Architectural Role**: It serves as the "Reference Input" in the control theory sense.
- **Data Source**: This stream originates from the user interface or a high-level planning agent. It typically arrives via REST or WebSocket and must be persisted in a state store accessible to the stream processor for the duration of the lifecycle.
- **Complexity**: Intents are often ambiguous ("Fix the bug in the auth service"). The Queen must often expand this intent into a "Contract of Operations" using a reasoning model before governance begins.

### 2.2 Stream 2: Worker Telemetry (The Observable)

Worker agents generate a massive volume of "thought traces" (Chain-of-Thought), tool invocation logs, and API responses. This is the "Observable" behavior of the system.

- **Characteristics**: High velocity, bursty, semi-structured (JSON/Log).
- **Architectural Role**: This acts as the "Process Variable." It represents the agent's perception of reality and its intended modifications to it.
- **Data Source**: Emitted by agent frameworks (LangChain, CrewAI, AutoGen) via instrumentation hooks (OpenTelemetry) into message buses like Kafka.
- **Complexity**: Telemetry is often noisy. A "reasoning trace" might contain explored-but-abandoned paths. The Queen must distinguish between internal deliberation (which is safe) and external tool execution (which carries risk).

### 2.3 Stream 3: Ground Truth / The Honey Store (The Reality)

The "Honey Store" represents the objective state of the environment—the database records, inventory levels, or code repositories. This is the only source of "Truth."

- **Characteristics**: Variable velocity (transaction-dependent), strictly structured (SQL schemas, Vector embeddings).
- **Architectural Role**: This provides the "Feedback Signal." It allows the Queen to validate if the agent's perceptions (Telemetry) align with reality.
- **Data Source**: Derived from transactional databases (PostgreSQL) via Change Data Capture (CDC) pipelines (Debezium) or real-time vector stores.
- **Complexity**: The Ground Truth is a moving target. While an agent is calculating a price based on data from *T₀*, the database might update at *T₁*. A naive governance system checking at *T₂* might flag the agent as incorrect, despite it being correct at the moment of decision. This necessitates temporal consistency in the stream processor.

### 2.4 The Fusion Synthesis

The Queen's core logic is a function **f(I, T, G) → D**, where:
- **I** = Intent
- **T** = Telemetry
- **G** = Ground Truth
- **D** = Decision

The difficulty lies in the fact that *T* and *G* are streams with different clocks. The architecture must align these streams temporally—joining the worker's action at time *t* with the database state at time *t*—to render a fair and accurate judgment.

---

## 3. Stream Processing Engines: The Nervous System

To implement the Queen, we require a computational substrate capable of ingesting these heterogeneous flows, maintaining state (the Intent), and executing complex logic (the Reasoning). We analyze two leading Python-native candidates: Bytewax and Pathway, contrasting them against the Java-based incumbent, Apache Flink.

### 3.1 Bytewax: Imperative Control over Timely Dataflow

Bytewax is an open-source Python framework built on top of Timely Dataflow, a Rust-based distributed stream processing engine. It is designed to offer the performance of Rust with the developer experience of Python.

#### 3.1.1 Architectural Internals

Bytewax leverages PyO3 to create a high-performance bridge between Python and Rust. It allows developers to define dataflows as Directed Acyclic Graphs (DAGs) of operators (`map`, `filter`, `reduce`, `stateful_map`).

- **Timely Dataflow**: The underlying engine is optimized for cyclic dataflows and low-latency coordination (sub-millisecond). This is crucial if the Queen needs to send feedback loops to workers (e.g., "Stop, retry with this constraint").
- **State Management**: Bytewax treats state as a first-class citizen. Operators can hold state (e.g., the User Intent) in memory, backed by SQLite or Kafka for fault tolerance. This enables the "Session Window" pattern, where the Queen accumulates all logs related to a specific `session_id`.

#### 3.1.2 Applicability to the Queen Node

Bytewax excels in scenarios requiring fine-grained, imperative control.

**Advantages:**
- **Pythonic**: It uses standard Python generators and functions, making it accessible to AI engineers who are not Big Data specialists.
- **Library Integration**: Because logic runs in a standard Python interpreter, the Queen can easily import libraries like `langchain` or `pydantic` directly within the stream operators.
- **Resource Efficiency**: Benchmarks suggest Bytewax consumes significantly less memory (up to 25x less) than JVM-based Flink, making it viable for edge deployments or cost-constrained environments.

**Limitations:**
- **Manual Temporal Logic**: While Bytewax supports windowing, implementing complex "time-travel" joins (e.g., joining logs with the valid database state at that millisecond) often requires manual implementation of watermarks and buffers.
- **Ecosystem**: Being newer, it lacks the vast connector ecosystem of Flink, though it supports Kafka and Redpanda natively.

### 3.2 Pathway: Reactive Dataflow and Differential Consistency

Pathway represents a paradigm shift towards **Declarative Dataflow**, powered by an engine based on Differential Dataflow. It is specifically engineered for "Live AI" and RAG pipelines.

#### 3.2.1 Architectural Internals

Pathway unifies batch and stream processing into a single syntax. Under the hood, it processes updates to data rather than just events.

- **Differential Dataflow**: This mathematical model allows Pathway to update outputs incrementally. If a late-arriving event changes the Ground Truth for a past timestamp, Pathway can automatically re-compute the downstream results. This provides "Exact Once" consistency guarantees that are mathematically rigorous.
- **The Reactive RAG Pattern**: Pathway includes an `xpack-llm` library that can ingest unstructured documents, chunk them, embed them (using models like OpenAI or HuggingFace), and maintain a Real-Time Vector Index directly in memory. This is a game-changer for the Queen architecture, as it allows the "Honey Store" to be queried semantically with zero latency.

#### 3.2.2 Applicability to the Queen Node

Pathway is the recommended engine for the Queen due to its handling of Ground Truth.

**Advantages:**
- **Automatic Temporal Consistency**: Pathway's `join_asof` operator allows the Queen to join Worker Telemetry with the Ground Truth stream, ensuring that the judgment is based on the database state exactly when the action occurred.
- **Built-in Vector Store**: The ability to index the "Honey Store" stream (via CDC) into a nearest-neighbor index within the stream processor eliminates the need for an external Vector DB (like Pinecone) for the hot governance loop.
- **Unified Codebase**: The same Python code works for backtesting the Queen on historical logs (Batch) and running her in production (Streaming), solving the Lambda Architecture maintenance burden.

**Limitations:**
- **Declarative Learning Curve**: The syntax is more akin to SQL or Pandas-on-streams, which requires a shift in thinking compared to Bytewax's imperative loops.

### 3.3 Comparative Synthesis

| Feature | Bytewax | Pathway | Implications for Queen Architecture |
|---------|---------|---------|-------------------------------------|
| **Core Paradigm** | Imperative Dataflow (Timely) | Declarative / Table (Differential) | Bytewax allows complex custom logic; Pathway simplifies state consistency. |
| **State Management** | Explicit (Developers manage state objects) | Implicit (Engine manages table updates) | Pathway reduces the risk of state drift in long-running governance sessions. |
| **Ground Truth Integration** | Requires external DB lookups or manual caching. | Native Reactive Vector Indexing. | Pathway enables "Stream RAG," critical for checking worker hallucinations against large corpora. |
| **Consistency** | Eventual / Processing Time | Exact Consistency / Event Time | Pathway prevents "false positives" in governance caused by late-arriving data. |
| **Ecosystem** | Python-centric, generic stream processing. | AI-centric, specialized for RAG/LLM pipelines. | Pathway's `xpack-llm` reduces boilerplate code for embedding and prompting judges. |

> **Decision**: For the centralized Queen agent, **Pathway is selected** as the primary engine due to its superior handling of state updates and native vector indexing, which simplifies the integration of the "Honey Store".

---

## 4. Stream Reasoning: The Online LLM-as-a-Judge

Mere data aggregation is insufficient for governance; the Queen must *reason*. Traditional stream processing uses regex or thresholds (e.g., "alert if value > 100"). Agent governance requires semantic evaluation (e.g., "alert if the agent is being rude" or "alert if the agent contradicts the database"). This is achieved through the **Online LLM-as-a-Judge** pattern.

### 4.1 From Offline Benchmarks to Online Guardians

"LLM-as-a-Judge" was pioneered for offline model evaluation (e.g., MT-Bench). In the Queen architecture, we move this logic online, executing it synchronously within the stream processing window.

#### 4.1.1 The Reasoning Loop

The Queen executes a continuous evaluation cycle for every significant worker action:

1. **Context Construction**: The Stream Processor (Pathway) creates a prompt containing:
   - **System Instruction**: "You are a governance auditor."
   - **Intent**: "User wants to refund order #123."
   - **Telemetry**: "Agent tool call: `refund_user(user_id=999)`."
   - **Ground Truth**: "Database Record: Order #123 belongs to `user_id=555`."

2. **Inference**: This prompt is sent to a Reasoning Model (The Judge).

3. **Verdict**: The Judge returns a structured output (JSON) containing a `verdict` (Pass/Fail), `confidence_score`, and `rationale`.

### 4.2 Stream Reasoning Patterns

The Queen employs different reasoning strategies based on the complexity of the verification.

#### 4.2.1 Reference-Based Evaluation (Fact Checking)

This pattern is used when Ground Truth is available. The Judge compares the Worker's output against the "Gold Standard" from the Honey Store.

- **Use Case**: An agent summarizes a document. The Queen retrieves the document (Ground Truth) and checks if the summary contains hallucinations.
- **Mechanism**: The Queen performs a Stream RAG lookup to fetch the relevant chunks of the document and asks the Judge: "Does the summary strictly adhere to the retrieved context?"

#### 4.2.2 Reference-Free Evaluation (Behavioral Analysis)

Used when judging soft constraints like tone, safety, or adherence to policy.

- **Use Case**: An agent interacts with a customer. The Queen checks for hostility or PII leakage.
- **Mechanism**: The Judge uses a "Rubric" prompt: "Rate the agent's response on a scale of 1-5 for politeness. Flag if any credit card numbers are revealed."

#### 4.2.3 Pairwise Comparison (Swarm Consensus)

In high-stakes scenarios, the Queen might spawn two worker agents to solve the same problem.

- **Mechanism**: The Queen acts as a discriminator. She presents both solutions to the Judge LLM and asks: "Which response is more accurate and concise?" This implements a "Mixture of Agents" pattern for quality assurance.

### 4.3 Optimizing Latency and Cost: The Tiered Judge System

Invoking GPT-4 for every log line is economically unviable and introduces latency (2-5 seconds). The Queen architecture implements a **Tiered Judiciary**:

| Tier | Model Architecture | Latency | Cost | Function |
|------|-------------------|---------|------|----------|
| **Tier 0: The Reflex** | Regex / Keyword / Embedding Cosine Similarity | < 10ms | Negligible | Fast filtering of PII, known bad patterns, and irrelevant logs. Implemented directly in Rust/Python logic. |
| **Tier 1: The Clerk** | Distilled Model (e.g., Llama-3-8B-Quantized, Haiku) | ~200ms | Low | Routine checks: Syntax validation, tone checks, simple intent alignment. Handles 80% of traffic. |
| **Tier 2: The Magistrate** | Reasoning Model (e.g., GPT-4o, Claude 3.5 Sonnet) | ~1-3s | High | Complex logic checks, ambiguity resolution, final approval for database writes. Triggered only by anomalies from Tier 1. |
| **Tier 3: The Council** | Ensemble / Chain-of-Thought (e.g., DeepSeek-R1, o1) | > 10s | Very High | Post-mortem analysis of critical failures, "Red Teaming" simulations. |

**Implementation in Pathway**: Pathway's `filter` and `map` operators route events to different tiers. A `filter(lambda x: x.score < 0.9)` after Tier 1 can seamlessly escalate low-confidence judgments to Tier 2.

### 4.4 Managing Bias in Judges

Online Judges are susceptible to biases that can warp governance.

- **Position Bias**: Judges tend to favor the first option presented. The Queen must randomize the order of inputs when performing pairwise comparisons.
- **Self-Preference Bias**: A GPT-4 Judge tends to rate GPT-4 outputs higher. The Queen should utilize cross-family judging (e.g., use Claude to judge GPT outputs) to ensure neutrality.

---

## 5. Designing the IMJDF Event Stream Protocol

To operationalize the governance decisions, a standardized communication protocol is essential. The "Queen" cannot rely on proprietary API calls; she must emit standard events that any worker (regardless of framework) can understand. We propose the **IMJDF Event Stream protocol**, encapsulated within the industry-standard CloudEvents specification.

### 5.1 The IMJDF Lifecycle

The acronym IMJDF stands for the five distinct phases of the governance loop:

1. **Intent**: The registration of a goal.
2. **Metric**: The observation of agent activity.
3. **Judgment**: The internal evaluation by the Queen.
4. **Decision**: The command issued to the swarm.
5. **Feedback**: The learning signal for system optimization.

### 5.2 Protocol Transport: CloudEvents

We utilize CloudEvents (v1.0) as the envelope format. This ensures interoperability across message brokers (Kafka, EventBridge) and serverless platforms (Knative). Using CloudEvents allows the Queen to sit on a generic event bus and govern agents deployed on AWS, Azure, or on-premise clusters transparently.

#### 5.2.1 Schema Specification

The IMJDF protocol defines a specific extension attribute `imjdf` to classify events.

**Base CloudEvents Attributes:**
- `type`: Reverse-DNS name indicating the event semantic (e.g., `org.queen.decision.v1`).
- `source`: URI of the emitting entity (e.g., `//queen/core/logic-engine`).
- `subject`: The specific worker or session being governed (e.g., `session:1234/worker:search-agent`).
- `datacontenttype`: `application/json`.

**IMJDF Extension Attributes:**
- `imjdfphase`: String (`INTENT`, `METRIC`, `JUDGMENT`, `DECISION`, `FEEDBACK`).
- `imjdftraceid`: Global correlation ID linking all events in a session.
- `imjdfrisklevel`: Integer (0-100) indicating the criticality of the event.

### 5.3 Payload Semantics: Integrating ACP and MCP

While CloudEvents handles the transport, the `data` payload requires a semantic protocol for agent interaction. We synthesize two emerging standards: **Agent Communication Protocol (ACP)** and **Model Context Protocol (MCP)**.

#### 5.3.1 The Role of ACP (Agent Communication Protocol)

ACP is designed for Agent-to-Agent orchestration and workflow management. It is "Async-first" and ideal for controlling the lifecycle of workers.

**Usage in IMJDF**: The `DECISION` phase utilizes ACP payloads. When the Queen decides a worker must stop, she emits a CloudEvent containing an ACP `ControlMessage`.

**Example Payload:**

```json
{
  "acp_type": "CONTROL",
  "command": "SUSPEND",
  "reason": "Hallucination detected in pricing logic",
  "resume_condition": "HUMAN_APPROVAL"
}
```

Workers adhering to ACP standards implement handlers for these control messages, ensuring they pause execution upon receipt.

#### 5.3.2 The Role of MCP (Model Context Protocol)

MCP focuses on connecting models to Data and Tools. It defines a Client-Host-Server topology where the "Host" (Queen) connects to "Servers" (Data/Tools).

**Usage in IMJDF:**
- **Honey Store Access**: The Queen acts as an MCP Host connecting to the Database (wrapped as an MCP Server). This standardizes how Ground Truth is fetched ("Resources").
- **Dynamic Tool Configuration**: The Queen can use MCP to dynamically reconfigure a worker. If a worker shows poor judgment, the Queen (via infrastructure hooks) can update the worker's MCP configuration to revoke access to sensitive tools (e.g., `delete_database`), effectively sandboxing the agent in real-time.

### 5.4 Detailed Schema Examples

#### 5.4.1 Phase: METRIC (Worker → Queen)

The worker reports its internal thought process.

```json
{
  "specversion": "1.0",
  "type": "org.queen.metric.v1",
  "source": "//swarm/worker/finance-agent",
  "subject": "session:8877",
  "id": "evt-001",
  "time": "2026-01-04T12:00:00Z",
  "imjdfphase": "METRIC",
  "imjdftraceid": "trace-uuid-99",
  "data": {
    "step": "reasoning",
    "content": "I need to calculate the tax. I will use the 'tax_calculator' tool.",
    "tool_use": "tax_calculator",
    "tool_input": { "amount": 100, "region": "NY" }
  }
}
```

#### 5.4.2 Phase: DECISION (Queen → Worker)

The Queen intercepts a potentially dangerous tool call.

```json
{
  "specversion": "1.0",
  "type": "org.queen.decision.v1",
  "source": "//queen/governor",
  "subject": "session:8877",
  "id": "evt-002",
  "time": "2026-01-04T12:00:01Z",
  "imjdfphase": "DECISION",
  "imjdftraceid": "trace-uuid-99",
  "imjdfrisklevel": 90,
  "data": {
    "acp_command": "BLOCK",
    "correction": "Region 'NY' requires 8.875% tax rate, but user profile says 'CA'.",
    "suggested_action": "Update region to 'CA'"
  }
}
```

---

## 6. The Honey Store: Techniques for Real-Time Ground Truth

The Queen's judgment is only as valid as her knowledge of the world. Static databases are insufficient; the Queen requires a "Live" view of reality. We implement this via Change Data Capture (CDC) and Stream RAG.

### 6.1 Change Data Capture (CDC): The Pulse of Reality

CDC is the process of capturing changes made to a database and streaming them to downstream systems. This turns a static database into an event stream.

- **Technology**: Debezium is the industry standard. It reads the Write-Ahead Log (WAL) of databases like PostgreSQL or MySQL.
- **Mechanism**: Every `INSERT`, `UPDATE`, or `DELETE` in the operational database is converted into a structured event message and pushed to Kafka.
- **Benefit**: This decouples the Queen from the production database load and ensures she sees data changes milliseconds after they commit. Unlike polling, CDC ensures no transient states are missed.

### 6.2 Stream RAG: The Reactive Vector Index

To query Ground Truth semantically (e.g., "Is this invoice consistent with the contract?"), the Queen needs a vector index. Traditional RAG architectures use an external Vector DB (e.g., Pinecone) which is updated via a periodic ETL job. This introduces "Data Lag"—the vector store might be minutes or hours behind the SQL database.

**Pathway's Reactive Indexing** solves this:

1. **Ingestion**: Pathway consumes the CDC stream from Debezium.
2. **Transformation**: It extracts text fields (e.g., `contract_body`) from the CDC events.
3. **Embedding**: It calls an embedding model to vectorize the text on-the-fly.
4. **Indexing**: It builds a Nearest Neighbor (KNN) index in-memory.
5. **Reactive Update**: Crucially, if a CDC `UPDATE` event arrives, Pathway finds the old vector, removes it, and inserts the new one instantly.

This enables **Stream RAG**: The Queen can perform vector searches against the absolute latest state of the database, ensuring her judgments are never based on stale data.

### 6.3 Time-Travel Debugging and "As-Of" Joins

A critical edge case in governance is the **race condition**.

**Scenario:**
1. A worker queries the DB at `10:00:01` and sees `Inventory=5`. It sells 5 items.
2. At `10:00:02`, the inventory is updated to `0` by another system.
3. The Queen evaluates the worker's action at `10:00:03`. If she looks at the current DB (`Inventory=0`), she might falsely accuse the worker of selling out-of-stock items.

**Solution**: Pathway supports **Temporal Joins** (specifically `asof_join`). The Queen joins the Worker Telemetry (timestamped `10:00:01`) with the Ground Truth stream *as of* `10:00:01`. This reconstructs the "World State" exactly as it existed when the agent made the decision, ensuring fair and accurate governance.

---

## 7. Technical Implementation Plan

This section provides a concrete roadmap for deploying the Queen architecture.

### 7.1 Infrastructure Stack

| Component | Technology |
|-----------|------------|
| **Compute** | Kubernetes Cluster (GKE/EKS) for orchestrating containers |
| **Stream Engine** | Pathway (deployed as a Docker container) handling the core governance logic |
| **Event Bus** | Redpanda or Apache Kafka serving as the central nervous system |
| **Ground Truth** | PostgreSQL (Source) + Debezium (CDC Connector) |
| **LLM Gateway** | LiteLLM or LangChain middleware to manage API keys and routing for Judge models |
| **Observability** | Arize Phoenix for visualizing the "Trace of Traces" (Worker traces + Queen judgment traces) |

### 7.2 Phase 1: The Passive Observer (Visibility)

**Objective**: Establish data flow without active blocking.

**Steps:**
1. Deploy Redpanda and create topics: `worker_logs`, `user_intents`.
2. Instrument Worker Agents (LangChain/CrewAI) to emit CloudEvents to `worker_logs`.
3. Deploy Pathway to consume `worker_logs`.
4. Implement a simple "Tier 1" Judge (Regex/Keyword) to flag errors.

**Deliverable**: A dashboard showing real-time agent activity and error rates.

### 7.3 Phase 2: The Connected Judge (Truth)

**Objective**: Integrate Ground Truth for semantic validation.

**Steps:**
1. Deploy Debezium Connect to stream PostgreSQL changes to `db_cdc` topic.
2. Update Pathway pipeline to ingest `db_cdc`.
3. Implement Stream RAG: Configure Pathway to vectorize critical text fields from the CDC stream.
4. Deploy a "Tier 2" LLM Judge (e.g., GPT-4o-mini) to compare Worker output against the Vector Index.

**Deliverable**: "Hallucination Alerts" triggered when workers contradict the database.

### 7.4 Phase 3: The Active Governor (Control)

**Objective**: Close the loop with automated intervention.

**Steps:**
1. Implement the IMJDF protocol. Define `DECISION` schemas.
2. Update Worker Agents with an ACP Handler. This handler subscribes to the `queen/decisions` topic and implements logic to `PAUSE` or `ROLLBACK` tool execution.
3. Configure Pathway to emit `DECISION` events when the Judge returns a "FAIL" verdict.

**Deliverable**: A self-healing system where the Queen automatically blocks valid-but-harmful agent actions.

### 7.5 Security Considerations

**Prompt Injection Defense**: The Queen is vulnerable to "Indirect Prompt Injection" via the Telemetry stream. If a user attacks a worker, and the worker logs the attack, the Queen reads it.

**Mitigations:**
- Implement a "Sandwich Defense" in the Judge prompt (placing log data between strict delimiters).
- Use a specialized "Security Classifier" model (e.g., Lakera Guard) as a pre-filter before the data reaches the main Judge LLM.
- **PII Masking**: Use Pathway's `apply` operator to run a PII masking function (using Presidio) on all logs before they are sent to external LLM providers.

---

## 8. Conclusion

The "Queen" architecture transforms the concept of AI governance from a passive, post-hoc auditing process into a proactive, real-time control system. By fusing User Intent, Worker Telemetry, and Ground Truth using a reactive engine like Pathway, we create a system that is both semantically aware and factually grounded. The integration of Online LLM-as-a-Judge provides the necessary cognitive reasoning, while CloudEvents and ACP provide the rigorous communication standards required for enterprise scale. This architecture ensures that as we delegate more autonomy to agentic swarms, we retain the capability to observe, judge, and correct their actions with mathematical precision and unwavering vigilance.

---

## Citations

| Reference | Description |
|-----------|-------------|
| 1 | Event-driven architectures and Orchestrator patterns |
| 5 | CDC and Debezium integration |
| 7 | Bytewax capabilities and architecture |
| 11 | Pathway architecture, Differential Dataflow, and Reactive RAG |
| 17 | LLM-as-a-Judge patterns and evaluation |
| 24 | CloudEvents specification and usage |
| 27 | Agent protocols (ACP, MCP) |
| 40 | Security and Prompt Injection |

---

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21. [LLM-as-a-Judge: A Practical Guide](https://towardsdatascience.com/llm-as-a-judge-a-practical-guide/) - Towards Data Science
22. [Latency Optimization in LLM Streaming](https://latitude-blog.ghost.io/blog/latency-optimization-in-llm-streaming-key-techniques/) - Ghost
23. [Deploy a large-sized LLM](https://docs.ray.io/en/latest/serve/tutorials/deployment-serve-llm/large-size-llm/README.html) - Ray Docs
24. [CloudEvents Primer](https://github.com/cloudevents/spec/blob/main/cloudevents/primer.md) - GitHub
25. [CloudEvents](https://cloudevents.io/) - CloudEvents.io
26. [CloudEvents v1.0 schema with Azure Event Grid](https://learn.microsoft.com/en-us/azure/event-grid/cloud-event-schema) - Microsoft Learn
27. [Agent Communication Protocol (ACP)](https://research.ibm.com/projects/agent-communication-protocol) - IBM Research
28. [The Agent Communication Protocol (ACP) and Interoperable AI Systems](https://macronetservices.com/agent-communication-protocol-acp-ai-interoperability/) - Macronet Services
29. [What is Agent Communication Protocol (ACP)?](https://www.ibm.com/think/topics/agent-communication-protocol) - IBM
30. [MCP and ACP: Decoding the language of models and agents](https://outshift.cisco.com/blog/mcp-acp-decoding-language-of-models-and-agents) - Outshift | Cisco
31. [What is Model Context Protocol (MCP)?](https://cloud.google.com/discover/what-is-model-context-protocol) - Google Cloud
32. [MCP vs A2A Clearly Explained](https://www.clarifai.com/blog/mcp-vs-a2a-clearly-explained) - Clarifai
33. [Dynamic MCP: Guiding AI Agents Through Complex, Stateful Tasks](https://portal.one/blog/dynamic-mcp-servers-tame-complexity/) - Portal One
34. [What Is Change Data Capture CDC for Real-Time Data](https://aerospike.com/blog/what-is-change-data-capture-cdc) - Aerospike
35. [The Collaboration Paradox](https://arxiv.org/pdf/2508.13942) - arXiv
36. [Crossing the Streams – Joins in Apache Kafka](https://www.confluent.io/blog/crossing-streams-joins-apache-kafka/) - Confluent
37. [pathwaycom/pathway](https://github.com/pathwaycom/pathway) - GitHub
38. [Models - LangChain Docs](https://docs.langchain.com/oss/python/langchain/models) - LangChain
39. [Arize, Vertex AI API](https://arize.com/blog/arize-vertex-ai-api/) - Arize
40. [Prompt Injection & the Rise of Prompt Attacks](https://www.lakera.ai/blog/guide-to-prompt-injection) - Lakera AI
41. [How to Red Team Your LLMs](https://checkmarx.com/learn/how-to-red-team-your-llms-appsec-testing-strategies-for-prompt-injection-and-beyond/) - Checkmarx
42. [The Benefits of Event-Driven Architecture for AI Agent Communication](https://www.hivemq.com/blog/benefits-of-event-driven-architecture-scale-agentic-ai-collaboration-part-2/) - HiveMQ
43. [Debezium Transactions Explained](https://medium.com/@marcelo.vicentim/debezium-transactions-explained-insert-delete-update-in-a-modern-data-lake-557751881e65) - Medium
44. [Arize AI - AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-kjmocii4mcw4s) - AWS
45. [Best Practices and Methods for LLM Evaluation](https://www.databricks.com/blog/best-practices-and-methods-llm-evaluation) - Databricks Blog
46. [Sending CloudEvents events to API destinations](https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-api-destinations-cloudevents.html) - Amazon EventBridge
