Five Powers and the Modern AI Stack

Something fundamental is changing in how humans interact with software. For decades, we built interfaces—buttons, menus, forms—and trained users to navigate them. Success meant making interfaces “intuitive.” But what if the interface disappeared entirely? What if users just stated what they wanted, and software figured out how to do it?

This transformation is possible because AI has evolved through three phases: Predictive AI (forecasting from data), Generative AI (creating content), and now Agentic AI (autonomous action). The agentic era combines five capabilities—the Five Powers—with a modular three-layer stack that makes composition possible. Understanding both the capabilities (what agents can do) and the architecture (how they’re built) is essential for building effective AI systems.

This lesson unifies two foundational frameworks: the Five Powers that enable autonomous orchestration, and the Modern AI Stack that provides the technical foundation. Together, they explain both why the UX→Intent shift is happening now and how to build systems that leverage it.

Part 1: From User Interface to User Intent

Traditional software interaction follows this model:

User → Interface → Action

Users navigate through explicit interfaces (menus, buttons, forms)
Every action requires manual initiation (click, type, submit)
Workflows are prescribed (step 1 → step 2 → step 3)
Users must know WHERE to go and WHAT to click
The interface is the bottleneck between intent and execution

Example: Booking a Hotel (Traditional UX)

Let’s walk through what this looks like in practice:

Open travel website
Click “Hotels” in navigation menu
Enter destination city in search box
Select check-in date from calendar picker
Select check-out date from calendar picker
Click “Search” button
Review list of 50+ hotels
Click on preferred hotel
Select room type from dropdown
Click “Book Now”
Fill out guest information form (8 fields)
Fill out payment form (16 fields)
Click “Confirm Booking”
Wait for email confirmation

Total: 14 manual steps, each requiring the user to know exactly what to do next.

The design challenge: Make these 14 steps feel smooth. Reduce friction. Optimize button placement. Minimize form fields. A/B test checkout flow.

This is “User Interface thinking”: The user must navigate the interface the developers designed.

The New Paradigm: User Intent

Now consider a fundamentally different model:

User Intent → Agent → Orchestrated Actions

Users state intent conversationally (“I need a hotel in Chicago Tuesday night”)
AI agents act autonomously (search, compare, book, confirm)
Workflows are adaptive (agent remembers preferences, anticipates needs)
Users describe WHAT they want; agents figure out HOW
Conversation replaces navigation

Example: Booking a Hotel (Agentic UX)

The same goal, achieved differently:

User: “I need a hotel in Chicago next Tuesday night for a client meeting downtown.”

Agent: “Found 3 options near downtown. Based on your preferences, I recommend the Hilton Garden Inn—quiet floor available, $189/night, free breakfast. Your usual king bed non-smoking room?”

User: “Yes, book it.”

Agent: “Done. Confirmation sent to your email. Added to calendar. Uber scheduled for Tuesday 8am to O’Hare. Need anything else?”

Total: 3 conversational exchanges replacing 14 manual steps.

What the agent did autonomously:

✅ Remembered user preferences (quiet rooms, king bed, non-smoking)
✅ Inferred need for transportation (scheduled Uber without being asked)
✅ Integrated with calendar automatically
✅ Understood context (client meeting = business district location)

This is “User Intent thinking”: The user expresses goals; the agent orchestrates execution.

Part 2: The Five Powers of AI Agents

Agentic AI can accomplish this transformation because it possesses five fundamental capabilities that, when combined, enable autonomous orchestration:

1. 👁️ See — Visual Understanding

What it means:

Process images, screenshots, documents, videos
Extract meaning from visual context
Navigate interfaces by “seeing” them
Understand diagrams and visual data

Example:

Claude Code reading error screenshots to debug issues
AI extracting data from invoices and receipts
Agents clicking buttons by visually locating them on screen

2. 👂 Hear — Audio Processing

What it means:

Understand spoken requests (voice interfaces)
Transcribe and analyze conversations
Detect sentiment and tone
Process audio in real-time

Example:

Voice assistants understanding natural speech
Meeting transcription and summarization
Customer service AI detecting frustration in tone

3. 🧠 Reason — Complex Decision-Making

What it means:

Analyze tradeoffs and constraints
Make context-aware decisions
Chain multi-step reasoning (if X, then Y, then Z)
Learn from outcomes

Example:

Agent choosing optimal hotel based on price, location, and preferences
AI debugging code by reasoning through error causes
Financial agents evaluating investment opportunities

4. ⚡ Act — Execute and Orchestrate

What it means:

Call APIs and use tools autonomously
Perform actions across multiple systems
Coordinate complex workflows
Retry and adapt when things fail

Example:

Claude Code writing files, running tests, committing to Git
Travel agents booking flights and hotels
E-commerce agents processing orders and tracking shipments

5. 💾 Remember — Maintain Context and Learn

What it means:

Store user preferences and history
Recall previous interactions
Build domain knowledge over time
Adapt behavior based on feedback

Example:

Agent remembering you prefer quiet hotel rooms
AI assistants referencing previous conversations
Personal AI learning your communication style

How the Five Powers Combine

Individually, each power is useful but limited.

Combined, they create something transformational: autonomous orchestration.

Hotel booking example breakdown:

Hear: User speaks request (“Find me a hotel in Chicago”)
Reason: Analyzes requirements (location, timing, context)
Remember: Recalls user prefers quiet rooms, king beds, downtown proximity
Act: Searches hotels, compares options, filters by criteria
See: Reads hotel websites, reviews, location maps
Reason: Evaluates best option considering all factors
Act: Books room, schedules transportation, updates calendar
Remember: Stores this interaction to improve future bookings

The result: A multi-step workflow orchestrated autonomously, adapting to context and user needs.

Part 3: The Modern AI Stack

The Five Powers explain what agents can do. The Modern AI Stack explains how they’re built. By early 2026, we have moved from “Chatbots with tools” to Protocol-Driven Autonomous Workers.

Layer 1: Frontier Models—The Reasoning Engines

Claude 4.5 / GPT-5.2 / Gemini 3: The foundation. These models now feature “Native Agentic Reasoning,” allowing them to pause, think, and call tools without needing a separate orchestration layer for simple tasks.

Layer 2: AI-First IDEs—The Context Orchestrators

Cursor / Windsurf / VS Code: These tools no longer just “see” your code; they act as the Skill Host. They are the environment where the models, tools, and local file systems meet.

Layer 3: Agent Skills—The Autonomous Workers

This is the most significant change. Instead of “Custom Agents,” we now build Modular Skills.

What the Agent Skills Standard (agentskills.io) Provides:

Progressive Disclosure: An agent doesn’t need to read 1,000 pages of documentation at once. It reads the “Skill Metadata” first (name and description). It only “loads” the full instructions and scripts when the task specifically requires them.
Skill Portability: A “SQL Expert” skill you write for Claude Code works instantly in Gemini CLI or OpenAI Codex.
Procedural Knowledge: Skills are stored as simple folders containing a SKILL.md file. They tell the agent how to do things (e.g., “Review this PR following the Google Style Guide”).

The 2026 Logic:

MCP = The “USB Cable” (Connects the agent to your Database/Slack/Jira).
Agent Skills = The “App” (Teaches the agent how to use that connection to achieve a goal).

Model Context Protocol (MCP): The Universal Connector

Everything in this stack is held together by MCP. In 2026, we have moved past the “plugin” era into the “protocol” era.

2026 Breakthrough: Bidirectional Sampling A major update to MCP in late 2025 introduced Sampling. This allows an MCP Server (like your database) to actually “ask” the LLM a question. For example: A database server can now ask the model, “I see this schema; should I optimize this specific index for the current query?” before returning results.

Feature	2024 (Pre-MCP)	2026 (Modern AI Stack)
Integration	Custom API for every tool	Standardized MCP Connectors
Vendor Lock-in	High (stuck with one ecosystem)	Zero (swap GPT for Claude instantly)
Data Access	Static RAG / Manual Uploads	Real-time, governed system access
Communication	One-way (Model → Tool)	Bidirectional (Tool ↔ Model)

Part 4: The Evolution—Why Now?

Understanding where we are helps explain why the UX→Intent shift is happening now.

Phase 1: Predictive AI

What it did: Analyzed historical data to forecast outcomes
Limitation: Could only predict, not create or act
Example: Netflix recommending movies based on watch history

Phase 2: Generative AI

What it does: Creates new content from patterns
Limitation: Generates when prompted, but doesn’t take action
Example: ChatGPT writing essays, code, or creative content when you ask

Phase 3: Agentic AI

What it does: Takes autonomous action to achieve goals
Breakthrough: AI shifts from tool to teammate—from responding to orchestrating
Example: Claude Code editing files, running tests, committing changes without asking for each step

The key difference: Earlier AI waited for commands. Agentic AI initiates, coordinates, and completes workflows autonomously.

This evolution unlocked the Five Powers working together, making the UX→Intent paradigm shift possible.

Part 5: The 2024 vs 2026 Shift—From Silos to Composition

2024: Tool Silos (Monolithic)

Bundled Capabilities: Each tool had its own “plugin” system. A “GPT Action” didn’t work in Claude.
Heavy Context: You had to paste massive instructions into your prompt every time to make the AI follow a specific workflow.
Vendor Lock-in: Moving from one agent to another meant rewriting all your “Custom GPTs.”

2026: Modular Stack (Composable)

Open Standards: The industry has converged on MCP and agentskills.io.
On-Demand Expertise: Agents “install” skills dynamically. You can say, “Install the Stripe-Support skill,” and your agent instantly knows the procedural steps for refunding a customer without you teaching it.
Cross-Platform Agency: You own your skills. They live in your repo as .md files, making your agents independent of any single model provider.

Part 6: Why This Shift Matters

The design challenge has shifted from “How do we prompt this?” to “How do we author the skill?”

The Skill Shift

2024 Focus (Prompting Era)	2026 Focus (Skill Era)
Prompt Engineering: Writing long, fragile “System Prompts.”	Skill Authoring: Writing structured `SKILL.md` files with clear YAML metadata.
Tool Integration: Writing custom API wrappers for every project.	Skill Discovery: Ensuring agents can find the right “Skill” for the job.
Manual Correction: Telling the AI “no, do it this way” repeatedly.	Constraint Engineering: Defining rigid workflows within a Skill that the AI must follow.

The Skill that Matters Most: Skill Architecture.

In 2026, high-level developers don’t just write code; they write the Skills that allow agents to write the code.

Before: You wrote a prompt: “Please check the database for errors.”
Now: You author a Database-SRE Skill that includes:

Metadata: “Use this when checking for Postgres performance bottlenecks.”
Logic: A Python script that pulls logs via an MCP connector.
Procedure: A step-by-step markdown guide for how to interpret those logs.

The result: You aren’t just giving an agent a task; you are giving it a permanent capability.

Core Concept

AI agents possess Five Powers (See, Hear, Reason, Act, Remember) that combine to enable autonomous orchestration, replacing navigation-based User Interfaces with conversation-based User Intent. These powers are delivered through a three-layer Modern AI Stack (Frontier Models, AI-First IDEs, Development Agents) connected by Model Context Protocol (MCP), which prevents vendor lock-in.

Key Mental Models

UX to Intent Paradigm Shift: Traditional software requires users to navigate interfaces (14 steps to book a hotel); agentic software lets users state intent conversationally (3 exchanges to achieve the same goal). The design challenge shifts from “make this interface intuitive” to “make this agent understand intent accurately.”
Five Powers Framework: See (visual understanding), Hear (audio processing), Reason (complex decision-making), Act (execute and orchestrate), Remember (maintain context and learn). Individually useful but limited; combined they create autonomous orchestration.
Three-Layer AI Stack: Layer 1 (Frontier Models — reasoning engines like Claude, GPT-5, Gemini), Layer 2 (AI-First IDEs — development environments like Cursor, VS Code, Zed), Layer 3 (Development Agents — autonomous workers like Claude Code). Layers are independent and composable.
MCP as USB for AI: Model Context Protocol is a universal standard connecting agents to data/services without vendor lock-in. Write an MCP integration once, any compatible agent can use it.
Predictive to Generative to Agentic Evolution: AI evolved from forecasting (Netflix recommendations) to creating (ChatGPT essays) to autonomous action (Claude Code editing, testing, committing). The agentic phase unlocked the Five Powers working together.

Key Facts

Hotel booking comparison: Traditional UX requires 14 manual steps; agentic UX reduces this to 3 conversational exchanges
2024 vs 2025 shift: From tool silos (vendor bundles everything) to modular stack (pick your model, IDE, and agent independently)
Current Frontier Models: Claude Opus 4.5 / Sonnet 4.5 (Anthropic), GPT-5.2 (OpenAI), Gemini 3 Pro / Flash (Google)
Current AI-First IDEs: VS Code (Microsoft), Cursor (Anystic), Windsurf (Codeium), Zed (Zed Industries)
MCP benefit: Before MCP, M models x N tools = M*N custom integrations. With MCP, M+N standardized connections

Critical Patterns

The Five Powers combine in sequences: Hear (request) -> Reason (analyze) -> Remember (recall preferences) -> Act (execute) -> See (read results) -> Reason (evaluate) -> Act (complete workflow) -> Remember (store for future)
The spec-writing skill is now paramount: “When user clicks button X, do Y” becomes “When user expresses intent Z (in any phrasing), agent understands and acts appropriately”
Competition drives innovation in modular stacks: when layers are independent, each layer improves separately and users benefit from best-of-breed selection
Removing any single power from an agent significantly degrades its capability — the powers are multiplicative, not additive

Common Mistakes

Thinking the Five Powers are just features when they are actually capabilities that must combine to enable autonomy (any single power alone is insufficient for orchestration)
Confusing the shift from UX to Intent as eliminating the need for good design (the design challenge changes from visual hierarchy to intent modeling and context management)
Assuming MCP eliminates all integration work (MCP standardizes the protocol, but you still need to build MCP servers for specific tools/services)
Treating the AI stack layers as tightly coupled when the key innovation is their independence and composability (you can switch models without changing your IDE or agents)