Agentic AI System Architecture
Understanding the design and terminology of AgenticFlowSim's intelligent automation approach.
Agentic AI systems represent a paradigm where tasks are accomplished by a collective of autonomous, intelligent agents. Each agent is specialized, possesses defined skills, and can communicate with other agents (A2A Communication). These agents operate within structured workflows to achieve complex business objectives, like those simulated in AgenticFlowSim for financial processes.
The diagram above illustrates a high-level view: multiple specialized agents collaborate, orchestrated within workflows, interacting with data sources and external systems to deliver a business outcome.
Agents are the core operational units. They are autonomous software entities designed to perform specific tasks or achieve particular goals.
Key Characteristics:
- Specialized: Focused on a narrow set of capabilities.
- Autonomous: Can operate independently to achieve their goals.
- Goal-Oriented: Driven by objectives defined within a workflow.
- Communicative: Capable of A2A interaction.
Agent Types in AgenticFlowSim:
- Simple AI : Driven by single prompts or basic AI models.
- Complex AI : Involves multi-step reasoning or multiple AI calls.
- Deterministic (API Call) : Executes pre-defined API interactions.
- Deterministic (Rule-based) : Follows fixed internal logic.
View an example agent: Data Ingestion Agent.
The "Agent Card" is a conceptual representation of an agent's definition, crucial for Agent-to-Agent (A2A) communication and system understanding. It typically includes:
- Identity: Unique ID, name.
- Purpose: Description, core task.
- Capabilities (Skills): Defined actions the agent can perform, along with their input/output data schemas.
- Implementation Details: How the agent achieves its tasks (e.g., AI model, API call).
- Evaluation Metrics: Performance indicators.
This structured definition allows other agents and the orchestration layer to understand how to interact with an agent effectively.
A skill represents a specific action or capability an agent can perform. It's the fundamental unit of an agent's functionality.
- Clear Interface: Each skill is defined by an input schema (the data it expects) and an output schema (the data it produces). These schemas act as a contract for interaction.
- Granularity: Skills can range from simple data transformations to complex AI-driven analyses.
- Discoverability: In mature agentic systems, skills might be registered and discoverable, allowing for dynamic composition.
In AgenticFlowSim, agent skills are explicitly listed on their "Agent Card" pages, showing example input and output JSON structures.
See an example on the KYC Analyzer Agent page under "Defined Agent Skills".
Workflows define how multiple agents collaborate to achieve a larger business process. They are typically represented as Directed Acyclic Graphs (DAGs).
- Nodes: Represent agents or specific tasks.
- Edges: Define the sequence of operations and data flow between nodes.
An Orchestration Layer (conceptually represented by the simulation engine in AgenticFlowSim) is responsible for:
- Executing agents in the defined order.
- Managing data handoffs between agents based on their skill schemas.
- Handling conditional logic and branching within the workflow.
Explore workflow examples on the Workflows page.
Agents need access to various data sources (internal, external, customer-provided) to perform their tasks.
The Model Context Protocol (MCP) (inspired by Anthropic AI's concept) is a way to provide rich metadata and instructions to an AI model (or agent) about the data it's processing. This context helps the model understand:
- The origin and nature of the data.
- How to interpret specific fields.
- Any constraints or special handling required.
- Relevant parameters for data retrieval or processing.
In AgenticFlowSim, the "Details" field of a data source provides a conceptual example of MCP parameters. View examples on the Customer Documents data source page.
A2A communication is how agents collaborate. This isn't necessarily "chatting" but rather a structured exchange of data and control.
- Skill Invocation: One agent might invoke a specific skill of another agent.
- Data Passing: Data is passed according to the target agent's skill input schema.
- Orchestrated: Usually managed by the workflow's orchestration layer.
The "Agent Card" and its defined "Skills" are fundamental to enabling effective A2A communication, as they define the interaction contracts.
Agentic systems are designed for continuous improvement. One powerful mechanism is Reinforcement Learning Fine-Tuning (RLFT) or similar feedback-driven model updates.
- Agent performance is monitored (accuracy, latency, cost, success rates).
- Outcomes are analyzed, and feedback (e.g., from human reviewers on escalated cases) is collected.
- This data is used to fine-tune the underlying AI models or adjust agent logic, enhancing efficacy over time.
Agent Cards in AgenticFlowSim include an "Evaluation" section that conceptualizes this, noting RLFT strategies and review cycles.
Effective agentic systems require clear governance. In AgenticFlowSim, workflows, agents, and data sources have associated responsible teams. These teams oversee:
- Development and maintenance.
- Performance monitoring and continuous improvement (including RLFT cycles).
- Adherence to compliance and ethical guidelines.
- Data quality and security for associated data sources.
This structured approach ensures accountability and facilitates the robust management of the entire agentic ecosystem.
- Framework: Next.js (App Router, React)
- UI Components: ShadCN UI
- Styling: Tailwind CSS
- AI Logic & Orchestration (Conceptual Backend): Genkit (with models like Google's Gemini) - Mocked in frontend for simulation.
- Language: TypeScript
- Icons: Lucide React