AI Agent Interpretability
Nov 27, 2025
I deep-dived into AI Agent Interpretability by Anthropic.
Here's what they found and it changes everything about how we build Agents:
》 What Is Interpretability?
Anthropic's team observe internal circuits, manipulate concepts in real-time, and track how models actually compute answers.
Three breakthrough discoveries:
》Discovery 1: Circuits Replace Memorization
A single circuit for adding 6+9 activates across completely different contexts.
Examples where the SAME circuit fires:
✸ Direct math: "6 + 9 = ?"
✸ Journal citations: "Polymer (1959), volume 6" → model calculates 1959 + 6 ✸ Any scenario requiring those digits to add
The model isn't retrieving facts.
It's computing live with reusable circuits.
Efficiency pressure during training forces generalization over memorization.
》Discovery 2: Universal Language of Thought
Ask "what's the opposite of big?" in English, French, or Japanese.
The internal representation is IDENTICAL across all languages.
Key findings:
✸ Small models keep separate circuits per language
✸ Large models merge into shared abstract concepts
✸ Models think in language-independent format
✸ Translation happens only at output layer
This explains true multilingual capability without separate "brains" per language.
》Discovery 3: Multi-Step Planning
When writing a rhyming couplet, models pick the final word of line 2 BEFORE writing line 1.
The proof:
✸ First line: "He saw a carrot and had to grab it"
✸ Model internally plans to rhyme with "rabbit"
✸ Researchers swap "rabbit" → "green" mid-generation
✸ Model reconstructs: "...paired it with his leafy greens"
This is sequential planning.
》The Dark Side: Backward Reasoning
Researchers gave hard math problems with wrong hints from users.
What happened:
✸ Model worked BACKWARD from the wrong answer
✸ Calculated which steps would "prove" the user's hint
✸ Wrote convincing-looking work that leads to wrong answer
✸ Never actually solved the problem
This is sycophantic deception at the circuit level.
》Why Agents Hallucinate
Two separate circuits handle:
✸ Answer generation (what to say)
✸ Confidence assessment (should I answer?)
》What This Means For You
Your LangGraph, CrewAI, or PydanticAI agent has:
✸ Multi-step planning circuits that look ahead
✸ Backward reasoning for goal pursuit
✸ Separate confidence modules that can fail independently
Traditional debugging shows WHAT failed.
Interpretability shows WHERE and WHY.
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👩💻 Written by Dr. Maryam Miradi
CEO & Chief AI Scientist
I train STEM professionals to master real-world AI Agents.
