A Large Language Model is at the core of any AI-Infused Application … but this is not enough. You also need: - Well crafted prompts guiding the LLM in the most precise and least ambiguous possible ways Prompts
A Large Language Model is at the core of any AI-Infused Application … but this is not enough. You also need: - Well crafted prompts guiding the LLM in the most precise and least ambiguous possible ways - A chat memory to "remember" previous interactions and make the AI service conversational Prompts Memory
A Large Language Model is at the core of any AI-Infused Application … but this is not enough. You also need: - Well crafted prompts guiding the LLM in the most precise and least ambiguous possible ways - A chat memory to "remember" previous interactions and make the AI service conversational - Data/Knowledge sources to provide contextual information (RAG) and persist the LLM state Prompts Memory Data Sources
A Large Language Model is at the core of any AI-Infused Application … but this is not enough. You also need: - Well crafted prompts guiding the LLM in the most precise and least ambiguous possible ways - A chat memory to "remember" previous interactions and make the AI service conversational - Data/Knowledge sources to provide contextual information (RAG) and persist the LLM state - External tools (function calling) expanding LLM capabilities and take responsibility for deterministic tasks where generative AI falls short Prompts Memory Tools Data Sources
Application A Large Language Model is at the core of any AI-Infused Application … but this is not enough. You also need: - Well crafted prompts guiding the LLM in the most precise and least ambiguous possible ways - A chat memory to "remember" previous interactions and make the AI service conversational - Data/Knowledge sources to provide contextual information (RAG) and persist the LLM state - External tools (function calling) expanding LLM capabilities and take responsibility for deterministic tasks where generative AI falls short - Guardrails to prevent malicious input and block wrong or unacceptable responses Prompts Memory Tools Data Sources
essence what makes an AI service also an Agent is the capability to collaborate with other Agents in order to perform more complex tasks and pursue a common goal
are programmatically orchestrated through predefined code paths and workflows LLMs dynamically direct their own processes and tool usage, maintaining control over how they execute tasks Workflow Agents
result + State Determine if done or next invocation Pool of agents Done Select and invoke (Agent Invocation) Autonomous Agentic AI – The Supervisor Pattern
Agent A Agent B Agent C Agent result Agentic Scope (Invocations +results) Pool of agents Done? Response Scorer Response Strategy State Scores Last, Score, Summary Input, response, action summary
all Pluggable Planner Workflow Supervisor GOAP P2P … Execution Layer Action Result State Agentic Scope Request Invoke Customizable by the framework (Quarkus) Agent A Agent B Agent C
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