Two Modes of AI Interaction

AI can work as a conversational partner (chat) or an autonomous executor (agent). Both are powerful. Knowing when to use each makes all the difference.

What Is Chat?

Chat is conversational AI:

• You ask a question, AI answers
• Back-and-forth dialogue
• Human remains in control of each step
• AI advises, human acts

Example: “How should I implement authentication for this API?” AI explains options, trade-offs, recommendations. You decide and implement.

What Is an Agent?

An agent is autonomous AI:

• You describe an outcome
• AI plans and executes steps
• AI uses tools (code, search, APIs)
• Human reviews results

Example: “Add authentication to this API” Agent analyzes codebase, implements auth, writes tests, creates PR. You review and approve.

When to Use Chat

Exploration and learning: “Explain how OAuth works” “What are the pros and cons of microservices?”

Decision support: “Should we use PostgreSQL or MongoDB for this use case?” “What’s the best approach for handling this error?”

Creative collaboration: “Help me brainstorm marketing copy” “What angles could we take for this feature announcement?”

Quick questions: “What’s the syntax for a Python list comprehension?” “How do I format a date in JavaScript?”

Sensitive decisions: When you want to think through each step before acting.

When to Use Agents

Multi-step tasks: “Research competitors and create a comparison matrix” “Refactor this module and update all the tests”

Repetitive work: “Generate API documentation for all endpoints” “Add error handling to all database queries”

Background processing: “Monitor this log file and alert me to errors” “Run these tests every time I save”

Complex implementations: “Build a new feature that does X, Y, and Z” (Agent breaks it down and executes)

When you know the outcome: When the “what” is clear, just not the “how.”

The Spectrum

It’s not always either/or:

[Full Chat] ←————————→ [Full Agent]
     ↑                      ↑
  Human does            Agent does
  all actions           all actions
     |                      |
     ↓                      ↓
 AI advises             Human reviews

In practice, you might:

• Chat to understand the problem
• Agent to implement the solution
• Chat to discuss the results
• Agent to refine based on feedback

Hybrid Workflows

Research → Implementation: Chat: “What’s the best way to implement caching?” Agent: “Implement caching using the approach we discussed”

Implementation → Review: Agent: “Build the user profile page” Chat: “Explain this component’s state management”

Debug → Fix: Chat: “Why might this test be failing?” Agent: “Fix the test based on what we identified”

Agent Governance

Agents need more governance than chat:

What can agents do?

• Which tools can they use?
• What systems can they access?
• What actions require approval?

How much autonomy?

• Can they execute code?
• Can they make network requests?
• Can they modify files?

What visibility?

• Can you see what they’re doing?
• Can you pause mid-execution?
• Are actions logged?

Calliope’s Approach

Chat Studio: Conversational AI for data and knowledge AI IDE Chat Mode: Pair programming through dialogue AI IDE Agent Mode: Autonomous coding tasks Deep Agent: Multi-step autonomous execution

Each tool supports the appropriate mode for its use case.

Decision Framework

Task requires conversation/guidance? → Chat
Task has clear outcome, multi-step? → Agent
Need to understand before acting? → Chat first
Know what you want, delegate execution? → Agent
High stakes, want control? → Chat
Repetitive, well-defined? → Agent

The Mode Selection Checklist

Choosing between chat and agent:

• Is the outcome clearly defined?
• Does this require multiple steps?
• Do I need to learn/understand something?
• Can I trust autonomous execution?
• Is this repetitive work?
• Do I need to review each step?

Right mode, right results.

Summary: Chat vs Agent

Use chat and agents in Calliope →