PERSONAL RESEARCH NOTES

AgenticXYZ

A coordinate system for Agentic AI.

Building agent-based knowledge collaboration and self-improving agent systems.

More than a name

A coordinate system.
My initials.
A research framework.

AxisMeaningRelationshipStageCore question
XCrossingCollaborate +
MeaningThe intersection of people and agents
RelationshipAgents with People
StageHuman in the Loop
Core questionHow agents collaborate with people to contribute, correct, and govern knowledge.
YYoursDelegate ×
MeaningAn agent for every person
RelationshipAgents for People
StageHuman on the Loop
Core questionHow agents build personalized memory and continue serving every person.
ZZeroEvolve ^
MeaningTowards self-improvement agentic AI
RelationshipAgents by Agents
StageHuman beyond the Execution Loop
Core questionHow agents improve agents and verify that each update is safe and effective.

A research framework—not a benchmarked maturity scale. The operators are metaphors, not measured performance claims.

X · Crossing

Agents with People.

Humans and agents shape intent, act, verify, and preserve provenance together.

Human in the Loop

Additive leverage1–10×
01Agentic Software
02Knowledge-based PR
03Agentic Runtime
04Agentic Internet

The role of the Harness

Agents should enter environments, take action, and observe consequences.

The Harness connects intent, tools, permissions, memory, state, and outcomes. That is how a model begins to participate in the world.

Y · Yours

Agents for People.

A persistent, user-owned personal agent. You define its goals, permissions, and intervention thresholds; it keeps context and acts over time.

Human on the Loop

Multiplicative leverage10–100×
User-owned memory24 / 7 continuityEscalation by judgmentPortable identity and history

Z · Zero

Agents improving Agents.

Z explores evidence-carrying, gated, and reversible self-improvement—not unconstrained self-modification.

Human beyond the Execution Loop

Compounding leverage100–1000×

Research direction · not a deployed capability claim

The open learning loop

Models now work inside the world.Most of what they experience never becomes verified knowledge.

Agents act, fail, recover, and get corrected. Too often, those outcomes remain logs instead of returning through a structured learning path.

The evolving digital knowledge center

Experience becomes knowledge only when it carries evidence.

01Foundation ModelGeneral capability
02Harness + SkillsScenarios and methods
03TrajectoryObservable experience
04SignalsEvidence and verifiers
Fast updateSkills / MemoryEditable, reversible, immediately reusable
Slow updatePost-trainingOnly stable patterns that generalize

Knowledge moves at three speeds

The model is the capability core—not the only place knowledge lives.

Slow

Model parameters

Stable, general, transferable capabilities.

Medium

Skills

Explicit, editable, versioned procedural knowledge.

Fast

Memory

Private, fresh, and task-specific context.

Four systems · one knowledge lifecycle

From an observed method to a validated capability.

01

Agentic Software

Discovers methods through human-agent interaction.

02

Knowledge-based PR

Packages intent, conditions, evidence, and counterexamples.

03

Agentic Runtime

Tests transfer across tools, schemas, and permissions.

04

Agentic Internet

Connects capabilities with provenance and authorization.

05

Post-training candidate

Promotion is considered only after transfer is verified.

Promotion is earned

Zero routine intervention. Never zero human authority.

General?Valid?Novel?Safe?Authorized?Stable?Compatible?
Keep in MemoryUpdate the SkillFreeze as EvaluationPost-training Candidate

Skills do not update model weights by themselves.

Private and time-sensitive knowledge stays external by default.

Internalization requires held-out gains without the original Skill.