AI Architecture

Agentic AI & Multi-turn Interactions

Moving beyond simple "prompt-response" models to autonomous, goal-oriented systems that maintain state and iterate.

1. Agentic AI

Agentic AI refers to AI systems that act as agents—they don't just answer questions; they perform actions to achieve a goal. They possess:

Core Characteristics

  • Autonomy: Ability to make decisions without constant human intervention.
  • Tool Use: Capability to use external tools (APIs, databases, code interpreters) to gather information or affect the world.
  • Planning: Breaking down complex goals into a sequence of executable steps (Chain of Thought).
  • Reflection: Reviewing outputs and self-correcting errors.
  • Memory: Maintaining context over long periods (Short-term & Long-term).
Goal: "Deploy this app to Cloud Run" | v [Agent Core] |--> Plan: 1. Build Docker image -> 2. Push to GCR -> 3. Deploy |--> Action: Run `docker build` (Tool: Shell) | | | (Error: Missing Dockerfile) | | |--> Reflection: "I need to create a Dockerfile first." |--> Action: Create Dockerfile (Tool: FileSystem) |--> Action: Run `docker build` again |--> Action: Run `gcloud run deploy` v Result: Success

2. Multi-turn Interactions

Multi-turn capability allows an AI to maintain a coherent conversation over multiple exchanges, remembering previous context, refining answers, and handling complex workflows that cannot be solved in a single shot.

Comparison

Single-turn (Stateless)

User: "Fix this code."

AI: "Here is the fixed code."

End of logic.

Multi-turn (Stateful)

User: "Fix this code."

AI: "I see a bug. Fixing it..."

User: "It's still failing test X."

AI: "Ah, I missed that edge case. I recall you mentioned X earlier. Updating..."

3. Applied to Nostra (Our Context)

How we leverage these concepts in the AI-Boosted Market Creation feature:

Workflow

  • User Intent: "Create a market for the election."
  • Agent Action: "I need more details. Which election? What candidates?" (Proactive questioning).
  • User Response: "2026 Seoul Mayor."
  • Agent Action: Uses Search Tool to find candidates (Oh Se-hoon, etc.).
  • Agent Action: Drafts market parameters (Title, Outcomes, Description).
  • User Feedback: "Add another candidate." (Multi-turn refinement).
  • Agent Action: Updates draft and executes Create Market Tool.
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