Implement an agentic solution

The presence_penalty parameter in Azure OpenAI model deployments is specifically designed to reduce the likelihood of the model repeating the same topics or phrases by penalizing tokens based on their presence in prior text.

This parameter works by applying a penalty to tokens that have already appeared in the generated text, regardless of how many times they've appeared. The key characteristic is that it penalizes based on presence (whether a token has appeared or not), not frequency. This encourages the model to introduce new topics and explore different concepts rather than circling back to previously mentioned ideas.

The presence_penalty accepts values typically between -2.0 and 2.0, where:
- Positive values discourage the model from repeating topics that have already been mentioned
- Negative values can encourage the model to stay on topic
- A value of 0 means no penalty is applied

Why the other answers are incorrect:

The second option incorrectly describes the temperature parameter, which controls randomness in token selection by adjusting probability distributions across the vocabulary.

The third option describes the frequency_penalty parameter instead, which penalizes tokens proportionally to how many times they've already appeared in the text. This is different from presence_penalty, which only cares about whether a token has appeared at all, not how many times.

The fourth option is incorrect because presence_penalty does not limit the maximum number of tokens or enforce any threshold on cumulative token usage. Token limits are controlled by separate parameters like max_tokens.

The primary purpose of semantic functions in the Semantic Kernel framework is to enable natural language prompts to be executed as reusable templated operations that can be chained together.

Semantic Kernel is designed to bridge the gap between traditional programming and AI capabilities by allowing developers to create semantic functions - these are essentially prompt templates that can be parameterized, reused, and orchestrated together. The key aspects are:

1. Natural language prompts as code: Semantic functions encapsulate prompts in a way that makes them callable and reusable
2. Templating: They support variables and parameters that can be injected into prompts
3. Chaining and orchestration: Multiple semantic functions can be combined together to create complex workflows
4. Reusability: Once defined, semantic functions can be called multiple times with different inputs

The other options contain inaccuracies:

The option about database queries and vector embeddings misrepresents the purpose - semantic functions are about executing prompts, not transforming database queries into embeddings. While embeddings may be used elsewhere in AI solutions, this is not the primary purpose of semantic functions.

The option mentioning state maintenance across sessions adds an element that is not a primary characteristic of semantic functions. While Semantic Kernel does support context and memory management, the core purpose of semantic functions themselves is about prompt execution and composition, not state management.

The option about converting templates into strongly-typed method signatures for API integration is misleading. While Semantic Kernel does provide programmatic interfaces, semantic functions are primarily about enabling prompt-based AI operations rather than API integration or strongly-typed signatures as their main purpose.

The correct solution involves implementing Azure Durable Functions with sub-orchestrations for each agent, using conditional branching logic to evaluate visual scanning results, employing fan-out/fan-in patterns for parallel execution, and passing aggregated context objects to downstream agents.

This approach is optimal because:

1. Complex Conditional Logic: Durable Functions natively supports conditional branching and complex orchestration patterns. When 30% of videos fail visual scanning, the orchestration can evaluate the result and adjust the workflow accordingly - proceeding with transcription and sentiment analysis using audio-only context, while adapting policy enforcement criteria based on available data sources.

2. Efficient Parallel Processing: The fan-out/fan-in pattern allows transcription and visual scanning agents to execute in parallel (both taking significant time - 15 and 10 seconds per minute of video respectively), then waits for both to complete before proceeding to sentiment analysis. This maximizes throughput for the 10,000 daily videos.

3. State Management: Durable Functions automatically handles orchestration state, checkpoint management, and recovery without requiring external databases. The aggregated context objects passed through activity function parameters ensure each downstream agent receives the complete data it needs.

4. Sub-orchestrations: For complex multi-agent workflows, sub-orchestrations provide logical separation and reusability while maintaining overall workflow coordination. This is crucial when policy enforcement needs to aggregate results from three different agents with varying data availability.

5. Scale and Cost: Durable Functions scales automatically and charges only for execution time, making it cost-effective for processing videos ranging from 30 seconds to 10 minutes.

Why the other approaches are less suitable:

The Logic Apps approach lacks the sophisticated conditional logic needed for handling the 30% failure rate in visual scanning with dynamic workflow adjustments. While it supports parallel branches, the data operation merging and service bus routing add unnecessary complexity and latency. Logic Apps is better suited for simpler integration scenarios rather than complex multi-agent AI orchestration.

The Container Instances with Event Grid solution introduces significant architectural complexity without corresponding benefits. Storing intermediate results in Cosmos DB and managing state through database documents creates additional latency and cost. The dead-letter queue approach for handling visual scan failures is overly complex compared to native conditional branching. This pattern would require extensive custom code for orchestration logic that Durable Functions provides out-of-the-box.

The Kubernetes Service with Kafka messaging pattern is over-engineered for this scenario. While it provides robust microservices architecture, it requires managing infrastructure complexity (Kubernetes cluster, Kafka brokers), implementing custom orchestration logic, and handling state management through message passing. The circuit breaker pattern through Polly libraries addresses resilience but doesn't solve the core orchestration challenge. This approach is more suitable for large-scale distributed systems with hundreds of microservices, not for coordinating four specialized agents with conditional dependencies.