The 9 Principles of Intelligent Agents
Building agents is easy. Building agents that work in production is hard. These 9 principles are the difference.
The Problem
You’ve built an agent. It handles the demo perfectly.
Then users get creative:
- They ask multi-step questions.
- They provide ambiguous context.
- They expect it to remember what happened 10 turns ago.
Your agent falls apart.
The issue isn’t your code. It’s that you’re building with intuition instead of principles.
The 9 Principles
These principles, distilled from Google’s agent research and production experience, form the foundation of intelligent agent design.
Principle 1: Model as Brain, Tools as Hands
The model reasons. The tools act.
| Component | Role | Anti-Pattern |
|---|---|---|
| 🧠 Model | Reasoning, planning, deciding | Letting the model do file I/O directly |
| 🤲 Tools | Executing actions, retrieving data | Having tools make decisions |
The Rule: Your LLM should decide what to do, not do it directly. Move execution to deterministic code.
Principle 2: The Agentic Loop
Perceive → Reason → Act → Observe → Repeat
Every intelligent agent follows this cycle:
Why It Matters: This loop enables self-correction. The agent sees its own results and adjusts.
Principle 3: Context Engineering > Prompt Engineering
What you give the model matters more than how you ask.
Traditional prompting focuses on phrasing. Context engineering focuses on information architecture.
| Prompt Engineering | Context Engineering |
|---|---|
| ”Please summarize this carefully…” | Give only the relevant 500 tokens, not 5000 |
| ”You are an expert analyst…” | Load the analyst skill with actual procedures |
| ”Remember to check the database…” | Actually query the database and inject results |
The Shift: Stop optimizing instructions. Start optimizing what information reaches the model.
📖 Deep Dive: For the complete treatment of sessions, memory types, and context management, see Context Engineering: Sessions and Memory.
Principle 4: Grounding in Reality
Agents that don’t touch reality hallucinate.
Grounding connects the model to real data:
| Grounding Type | What It Provides | Example |
|---|---|---|
| RAG | Document knowledge | ”According to our policy doc…” |
| Tools | Live system state | ”The current stock price is…” |
| Observation | Action results | ”The file was successfully created.” |
The Anti-Pattern: Agents that reason without grounding are “open-loop”—they generate plausible-sounding nonsense.
Principle 5: Fail Explicitly, Recover Gracefully
Every tool call can fail. Plan for it.
The Rules:
- Set max retries (typically 2-3)
- Use exponential backoff for rate limits
- Have a fallback when all else fails
- Log everything for debugging
Principle 6: Least Privilege for Tools
Give agents only the tools they need, only when they need them.
| Scenario | ❌ Dangerous | ✅ Secure |
|---|---|---|
| Code assistant | Full file system access | Read/write to project folder only |
| Database agent | DELETE permissions | Read + parameterized writes only |
| Email agent | Send to anyone | Send to pre-approved domains only |
Why: Agents are non-deterministic. A confused agent with broad permissions is a security incident waiting to happen.
Principle 7: Observability > Debuggability
You can’t debug what you can’t see.
Production agents need full telemetry:
| Layer | What to Log |
|---|---|
| Request | User input, session ID, timestamp |
| Reasoning | Model’s internal plan/thoughts |
| Tool Calls | Which tool, parameters, response |
| Response | Final output, latency, token count |
The Payoff: When an agent misbehaves at 3 AM, logs tell you exactly where the chain broke.
Principle 8: Trajectory Evaluation
Judge the journey, not just the destination.
Traditional evaluation: “Is the final answer correct?”
Trajectory evaluation: “Did the agent take sensible steps to get there?”
| Evaluation Type | What It Checks | Catches |
|---|---|---|
| End-to-End | Final output correctness | Wrong answers |
| Trajectory | Intermediate steps quality | Lucky guesses, inefficient paths |
Example: An agent might get the right answer by accident (hallucinated a number that happened to be correct). Trajectory evaluation catches this.
Principle 9: Human-in-the-Loop by Design
Some decisions should never be fully automated.
Build approval gates into high-stakes workflows:
The Litmus Test: Would you trust a junior employee to do this unsupervised? If not, add human approval.
Principles in Action: Industry Examples
| Principle | 🏦 Banking | 🛒 Retail | 🎓 Education |
|---|---|---|---|
| Model = Brain, Tools = Hands | Model decides risk level; tool calls credit bureau | Model recommends product; tool checks inventory | Model designs lesson; tool updates gradebook |
| Grounding in Reality | RAG pulls current rate policies | RAG retrieves product catalog | RAG fetches student history |
| Least Privilege | Agent can READ accounts, cannot TRANSFER funds | Agent can view orders, cannot issue refunds > $50 | Agent can read grades, cannot modify transcripts |
| Human-in-the-Loop | Loans > $100K require human approval | Returns > $500 escalate to manager | Grade changes require instructor sign-off |
The High-Stakes Pattern
| Domain | Auto-Execute | Requires Human |
|---|---|---|
| 🏦 Banking | Balance inquiry, statement generation | Wire transfer > $10K, account closure |
| 🛒 Retail | Order status, product recommendations | Refund > $500, price override |
| 🎓 Education | Practice quiz, study tips | Final grade submission, academic warning |
Key Takeaways
- ✅ Model = Brain, Tools = Hands: Separate reasoning from execution.
- ✅ The Agentic Loop: Perceive → Reason → Act → Observe creates self-correction.
- ✅ Context Engineering: What reaches the model matters more than how you phrase it.
- ✅ Grounding: Connect to reality or your agent hallucinates.
- ✅ Fail Gracefully: Every tool fails. Have retries and fallbacks.
- ✅ Least Privilege: Limit what agents can do to limit what can go wrong.
- ✅ Observability: Log everything. Debug with confidence.
- ✅ Trajectory Evaluation: Judge the process, not just the output.
- ✅ Human-in-the-Loop: Keep humans in control of high-stakes decisions.
What’s Next
- 📖 Previous article: Skills: Progressive Context Disclosure — On-demand procedural knowledge.
- 📖 Next article: Production Patterns: Resilience & Quality — Build systems that catch failures before users do.
- 💬 Discuss: Which principle is most often violated in your experience?
References
210: 1. Google Cloud Research — Introduction to Agents (2025). Defines the Agentic Loop and 5-level taxonomy. 211: 212: 2. Google Cloud Research — Agent Quality (2025). Introduces trajectory evaluation and LLM-as-a-Judge patterns. 213: 214: 3. Anthropic — Building Effective Agents (2024). Emphasizes tool design and failure handling.
❓ Frequently Asked Questions
What are the 9 principles for building intelligent AI agents?
1) Separate model reasoning from tool execution, 2) Define the agentic loop, 3) Context engineering over prompt engineering, 4) Ground in reality, 5) Graceful failure handling, 6) Least privilege for tools, 7) Observability, 8) Trajectory evaluation, 9) Human-in-the-loop design.
What is the difference between prompt engineering and context engineering?
Prompt engineering optimizes single-turn instructions. Context engineering manages the entire context window across a session—including memory, tool outputs, and conversation history.
Why is human-in-the-loop important for AI agents?
Critical actions require human approval. This prevents costly mistakes, builds trust, and ensures compliance with enterprise governance requirements.
💬 Join the Discussion
📣 Prefer LinkedIn? Connect and discuss on LinkedIn →