Agent Mode: The Evolution Beyond AI Mode

Simon Willison just wrote about Google’s new AI Mode, calling it ‘good, actually.’ He’s right - it’s fast, it’s smart, and it finally makes Google competitive with ChatGPT’s search capabilities. But while everyone’s celebrating AI Mode, they’re missing the bigger picture.

AI Mode is the bridge, not the destination.

What comes next will make AI Mode look like a calculator compared to a computer. It’s called Agent Mode, and it represents the fundamental shift from AI that answers questions to AI that completes tasks. This isn’t speculation - it’s the inevitable evolution that every major platform will undergo.

Table of contents

From AI Mode to Agent Mode

Let’s be clear about what we’re dealing with today. AI Mode, whether it’s Google’s implementation or ChatGPT’s search, follows a predictable pattern:

1. You ask a question
2. The system runs searches (usually hidden from you)
3. It synthesizes information
4. You receive an answer

This is revolutionary compared to traditional search, but it’s still fundamentally passive. You’re getting information, not action. You’re receiving knowledge, not results.

Agent Mode changes this equation entirely:

1. You define a goal
2. The agent creates a plan
3. It executes multiple steps autonomously
4. It adapts based on results
5. You receive a completed task

The difference is profound. AI Mode tells you how to fix a bug. Agent Mode fixes the bug. AI Mode explains how to analyze data. Agent Mode delivers the analysis. AI Mode is a brilliant assistant. Agent Mode is an autonomous worker.

To understand this distinction deeply, see Agentic AI which introduces the fundamental concepts, and Agentic Workflows: The Power of Determinism which explains why deterministic execution beats probabilistic magic.

The Agentic Experience Revolution

When Simon complained that Google’s AI Mode won’t show you the ‘5 searches’ it’s running, he touched on something crucial without realizing it. That lack of transparency is a feature of AI Mode, not a bug. AI Mode is designed to hide complexity, to present you with clean, synthesized answers.

Agent Mode takes the opposite approach. Transparency isn’t optional - it’s essential. When an agent is performing actions on your behalf, you need to see what it’s doing. You need control. You need the ability to intervene. This is what I call Agentic Experience (AX) - designing for agents as the new users of our systems.

Imagine this scenario with current AI Mode: ‘How do I optimize this database query?’ Result: A detailed explanation of optimization techniques.

Now imagine Agent Mode: ‘Optimize this database query for me.’ Result:

• Analyzing current query performance…
• Identified 3 bottlenecks…
• Creating indexes on columns X, Y, Z…
• Rewriting query to use JOIN instead of subquery…
• Running performance tests…
• Query now executes 87% faster. Here’s what changed: [detailed diff]

You’re not just informed. Your work is done.

ReAct and the Agent Loop

The technological leap from AI Mode to Agent Mode hinges on something called the ReAct pattern - Reason + Act. It’s the difference between a system that thinks and a system that thinks and then does.

In AI Mode, reasoning is the end goal. The system reasons about your question, searches for information, reasons about that information, and presents conclusions. It’s a one-way street ending in text output.

Agent Mode operates in loops:

• Reason: What needs to be done?
• Act: Execute an action
• Observe: What was the result?
• Reason: Did that work? What’s next?
• Act: Execute the next action
• Repeat: Until the goal is achieved

This isn’t just an incremental improvement. It’s a fundamental reimagining of how AI systems operate. Each loop isn’t predetermined - the agent adapts based on what it observes. When something fails, it tries another approach. When it discovers new information, it adjusts its plan.

For a deeper exploration of how these systems evolve, read Evolution of Agentic Systems which shows why graph-based architectures are the inevitable next step.

What Agent Mode Looks Like in Practice

Google’s AI Mode proudly announces it’s ‘running 5 searches’ but won’t tell you what they are. This opacity would be unacceptable in Agent Mode. Here’s what the same interaction looks like when agents are involved:

AI Mode (Today): ‘Running 5 searches…’ [Hidden: what searches, what sources, what logic] ‘Here’s your answer: [synthesized response]’

Agent Mode (Tomorrow):

Planning: Breaking down your request into 7 subtasks...
Step 1/7: Searching documentation for API endpoints
  ✓ Found 3 relevant endpoints
Step 2/7: Testing endpoint availability
  ✓ All endpoints responding
Step 3/7: Crafting optimal query parameters
  ✓ Parameters optimized for your use case
Step 4/7: Executing primary data fetch
  ⚠ Rate limited - implementing exponential backoff
Step 5/7: Retrying with adjusted parameters
  ✓ Data retrieved successfully
Step 6/7: Transforming data to requested format
  ✓ Transformation complete
Step 7/7: Validating output against requirements
  ✓ All requirements met

Task completed. Would you like to:
- See the detailed execution log
- Modify and re-run with different parameters
- Save this workflow for future use

The difference isn’t just transparency - it’s control. You can intervene at any step. You can modify the plan. You can turn a one-time execution into a reusable workflow. This is precisely why Agentic Workflows are so powerful - they turn discovered agent behaviors into reliable, repeatable processes.

The Corporate Evolution Path

Every major tech company will follow this same trajectory:

Stage 1: Traditional Search

• Keywords in, links out
• Users do all the synthesis
• Google circa 2015

Stage 2: AI Mode (We are here)

• Natural language in, synthesized answers out
• Hidden complexity, polished responses
• Google AI Mode, ChatGPT Search, Perplexity

Stage 3: Agent Mode (Coming soon)

• Goals in, completed tasks out
• Transparent execution, user control
• The next battleground

This evolution isn’t random - it follows the predictable path I outlined in Evolution of Agentic Systems. We’re moving from simple LLM + tools to sophisticated graph-based architectures.

Google isn’t building AI Mode because they love answering questions. They’re building it because it’s the necessary foundation for Agent Mode. The same infrastructure that fetches and synthesizes information can be repurposed to fetch and execute actions.

Microsoft understands this. That’s why Copilot isn’t just about chat - it’s about agents working across Office suite. OpenAI gets it too. GPTs were the preview; true agents are the product.

The company that successfully transitions from AI Mode to Agent Mode first wins the next decade of computing. See how this transformation is already happening in Agentic Evolution.

Building for Agent Mode Today

If you’re building products today, you need to start thinking beyond the AI Mode paradigm. Your APIs aren’t just being consumed by developers anymore - they’re being consumed by agents. Your interfaces aren’t just for humans - they’re for agentic workflows. I’ve written extensively about this in Agent-Friendly CLI Tools - how to build tools that agents can actually use effectively.

This means:

Design for Discoverability: Agents need to understand what your service does. Not through marketing copy, but through structured, machine-readable descriptions. The emerging llms.txt standard is just the beginning.

Embrace Determinism: Agents hate surprises. Your API that returns different formats based on moon phases might seem clever, but it’s agent-hostile. Predictable beats clever every time. This is the core thesis of Agentic Workflows - determinism is your friend in production.

Provide Feedback Loops: Agents learn through interaction. Give them clear success/failure signals. Make errors informative, not poetic. ‘Invalid request’ tells an agent nothing. ‘Missing required parameter: user_id’ enables recovery.

Think in Workflows: Stop designing individual endpoints. Design complete workflows. An agent shouldn’t need to reverse-engineer your business logic to complete a task.

Transparency Over Magic: AI Mode hides complexity. Agent Mode exposes it. Your service should provide detailed logs, clear state transitions, and observable progress indicators.

The Trust Transition

The biggest challenge in moving from AI Mode to Agent Mode isn’t technical - it’s psychological. AI Mode feels safe because it’s passive. It gives you information, but you retain control. Agent Mode requires a new kind of trust.

You’re not just trusting the AI to be correct. You’re trusting it to take actions. This is why transparency becomes critical. Users need to see what agents are doing, not because they’ll understand every step, but because visibility creates accountability. This trust problem is why Simulation Testing and Stop Hallucinating, Start Simulating are critical - we need deterministic, testable agent behaviors.

The platforms that succeed in Agent Mode will be those that solve the trust equation:

• Clear boundaries on what agents can and cannot do
• Rollback capabilities for every action
• Human approval gates for critical operations
• Detailed audit logs for compliance and debugging

Deep Dives: Learning from Today’s Agent Implementations

To see how different companies are approaching the agent challenge today, explore these deep analyses:

• ChatGPT Agents: A GUI-First Approach - How OpenAI is building agents through graphical interfaces
• Claude Code Agents: A Terminal-First Approach - Anthropic’s command-line agent strategy
• Kiro Agents: Spec-Driven Development - How specification-driven approaches create reliable agents

Each represents a different philosophy on the path to Agent Mode, and understanding their tradeoffs will help you build better agentic systems.

Your Next Steps

AI Mode is good, actually. Simon’s right about that. But if you’re stopping there, you’re missing the revolution.

Start preparing for Agent Mode now:

1. Audit your APIs: Are they agent-friendly or human-centric?
2. Design for automation: What workflows could agents handle entirely? Start with Proactive Agents to understand agents that act before you ask.
3. Build in transparency: Can users see and control what’s happening?
4. Create safety rails: What constraints prevent agents from causing harm? Consider the Three Laws for Agentic AI as a governance framework.
5. Think in loops: How can your service support iterative, adaptive interaction?

The transition from AI Mode to Agent Mode isn’t a question of if, but when. The companies investing heavily in AI Mode today - Google, Microsoft, OpenAI - aren’t doing it for better search results. They’re building the infrastructure for the agentic revolution.

AI Mode shows you the answer. Agent Mode delivers the result.

AI Mode is the Google of 2025. Agent Mode is the Google of 2030.

The future isn’t about better answers. It’s about completed work. And that future is closer than you think.