AI Agents | Xi Zhang

📝 New Blog Just Dropped!

Wed, 04 Mar 2026 00:00:00 +0000

🧠 TL;DR

Full research autonomy will happen — but it won’t come from a pre-built product. It will emerge from your own harness: a system that absorbs your judgment, encodes your taste, and compounds with every run. The question is not whether this shift is coming. The question is whether you are building the harness to grow with it.

Dive deeper in the full post: Harness Vibe Research 📟

Opinions on my own

Harness Vibe Research 📟

Wed, 04 Mar 2026 00:00:00 +0000

TL;DR — Full research autonomy will happen — but it won’t come from a pre-built product. It will emerge from your own harness: a system that absorbs your judgment, encodes your taste, and compounds with every run. The question is not whether this shift is coming. The question is whether you are building the harness to grow with it.

We are making the same mistake twice. In coding, we over-focused on model capability and under-focused on system design. Now in research, we are over-focusing on autonomy and under-designing human-AI coupling.

More autonomy alone often increases entropy. Too little AI integration creates almost no leverage. The real multiplier is harness design: how humans and agents share context, verification, and control.

The signal is already here

This shift is not theoretical. Multiple teams in different settings are reporting the same pattern:

Sakana AI reported AI Scientist v2 and discussed both progress and quality limits in autonomous paper-generation workflows.
Google shared Gemini Deep Think results on open math problems and shipped AI co-scientist — a multi-agent system on Gemini 2.0 with self-critique, Elo-based ranking, and human-in-the-loop design. Its drug repurposing and target discovery hypotheses were experimentally validated in the lab.
Orchestra demonstrated an ACL submission timeline built with an AI co-scientist workflow.
Open ecosystems such as K-Dense and Biomni are treating reusable “skills” and know-how libraries as core research infrastructure, not optional add-ons.
Even at the execution layer, Anthropic’s Programmatic Tool Calling is rethinking how agents compose actions — optimizing for token efficiency and multi-step orchestration.

Different institutions, different domains, same direction: people are shipping systems, not just demos.

What coding agents already taught us

Before research agents, coding agents already exposed the key lesson.

OpenAI publicly described building large production systems with AI-generated code and emphasized environment and harness design as the engineering bottleneck.

LangChain has shown that major benchmark gains can come purely from harness upgrades: self-verification loops, better context injection, loop guards, and reasoning budget control. Same base model class, better orchestration. See this blog.

The same model became dramatically more capable — not because it got smarter, but because the system around it got better.

The open source community is already far ahead. Projects like OpenClaw — an open-source personal AI assistant with a skills-first architecture and full agent autonomy — are going viral. Developers have dozens of harness prototypes to choose from. Research, by contrast, is still catching up. Most research teams are either building from scratch or relying on closed, single-purpose tools. The infrastructure gap is real.

The autonomy paradox

There is a natural instinct to push to full automation: remove humans and let agents run end to end.

But in research, higher autonomy frequently increases drift risk:

agents can optimize for easy novelty instead of meaningful questions,
outputs can look polished while missing key checks,
small errors compound across long execution chains.

Several autonomous research systems have openly acknowledged this. Analemma AI’s FARS has now been running continuously for 400+ hours, consumed 20.8 billion tokens (~$179K), generated 425 hypotheses and produced 150+ papers. Earlier in the run, the first 100 papers scored 5.05 on ICLR review standards — above the average human submission (4.21) but below the acceptance line (5.39). Consistent quantity, stable quality, but no breakthrough. Human review remains mandatory before publication.

FARS livestream dashboard — 404 hours, 159 papers, 425 hypotheses, 20.8B tokens, $179K cost, 154 GPUs at 62% utilization

The opposite extreme also fails. If AI is only autocomplete attached to an unchanged workflow, it speeds typing but does not change research throughput or insight quality.

The Autonomy Paradox — Full Automation creates chaos, No AI creates exhaustion, Harness is the sweet spot

So the design problem is not autonomy maximization. The design problem is controllable compounding.

Harness, not workflow

A workflow is a sequence of steps. A harness is a coupling layer between human intent and AI execution.

A good research harness is not rigid. It is adaptive and inspectable, with clear handoff points between person and agent.

Operationally, this means four concrete components:

Skills as reusable domain procedures Encode expert know-how in structured artifacts: what to test, which tools to call, what evidence thresholds to use, and how to interpret failure.
Verification loops tied to hypotheses Require each run to check claims against the original question, edge cases, and relevant prior literature before outputs are accepted.
Context engineering as an environment checklist Define available data, tools, constraints, and success criteria up front so the agent reasons inside the real research boundary.
Tracing plus iteration Capture run traces, inspect failures, and update the harness. Treat every run as training data for the system design itself.

Four components of a research harness — Skills, Verification, Context, Tracing — with the researcher at the center

When these four pieces are in place, quality improves because the system becomes easier to debug, not because the model “became smarter” overnight.

AI in the human’s loop

“Human in the loop” often implies AI is the main actor and people are safety checkpoints.

For research, a better framing is AI in the human’s loop.

The human owns direction, taste, and significance judgments. The AI expands search, drafts alternatives, and accelerates execution.

A practical division of labor:

Human chooses the question; AI maps the possibility space.
Human decides what is meaningful; AI runs and refines candidate paths.
Human owns the published claim; AI supports drafting, formatting, and reproducibility packaging.

Paradigm shift — from “Human in the Loop” to “AI in the Human’s Loop”

These decisions are not arbitrary. A researcher operates under real constraints — limited funding, finite compute, a specific disciplinary background, access to particular platforms and collaborators. What looks like “taste” is actually constrained optimization: choosing the best move given the resources at hand. That is precisely why the human must stay in the driver’s seat — no model has access to these constraints.

Human oversight in this model is O(1) — a constant-cost operation that grounds the entire system, regardless of task complexity.

This is not semantics. It changes architecture, evaluation, and team roles.

Completeness over perfection

Many teams perfect one stage — usually generation or writing — while leaving the rest manual and fragile. But research is a cycle: ideation → experiment design → execution → analysis → writing → revision. A break anywhere stalls everything.

An imperfect but complete harness usually outperforms a perfect partial tool, because researchers can intervene at any node and keep the full cycle moving. Phylo’s Biomni and its Know-How Library already prove this: lifecycle coverage beats single-stage depth.

Three steps you can start today

The core idea: encode your research taste into the harness. Let the harness drive the model. You drive the vibe research.

Talk to your system. Encode your taste as a Skill. Pick one workflow you repeat every week — baseline replication, literature comparison, whatever it is. Instead of running it yourself, sit down and tell your agent: here is how I judge quality, here are the signals I watch, here is where I pause to think. Write that into a Skill file. You are turning your research intuition into an executable asset.
Give the system authority, but bind it to verification. Let the agent run autonomously — but require it to answer two questions before delivering results: does this address the original hypothesis? Is there an edge case being ignored? You do not need to watch every step. You just need a gate at the exit. Trust the system, but verify the output.
Review traces. Evolve your harness. Keep run logs. After every run, ask: where did the agent get stuck? Which Skill was underspecified? Update one rule, one context block, or one verification step. Each iteration compounds — your harness absorbs more of your judgment every cycle.

Through this loop of encoding, delegating, and refining, you will notice something shift: it is not that the model got smarter — it is that your system became more like you.

Build the soil now

This is not just about us. In K-12 education, the harness revolution is already here. Alpha School replaced classroom lectures with AI tutors and turned teachers into motivational guides — their students spend only two to three hours a day on academics yet consistently score in the 99th percentile. Harvard found that students learning physics with an AI tutor outperformed those taught by a professor. The coupling of human guidance and AI execution is already surpassing traditional human-only performance in education. Research is next.

These students have not grown up yet. But when they do, they will arrive in labs and research groups expecting human-AI coupling as the default — because it is all they have ever known. If the research world has not built its own harness infrastructure by then, it will be the system that is behind, not the people. That is why we need to start now: not just to accelerate our own work, but to cultivate the soil so the next generation of researchers inherits compounding, not a cold start.

The tools are ready — the harness gap remains

The ecosystem already has most building blocks: reusable skills, harness engineering practices, multi-agent orchestration frameworks, and programmatic tool calling. Tools like Deep Agents already provide the agent harness infrastructure — planning, context management, sub-agent delegation, persistent memory — built on Langchain.

What is still missing in many teams is the integration layer that keeps humans and agents in tight, high-frequency collaboration.

Do not just vibe. Harness the vibe.

We are excited to see — and contribute to — an open-source research harness built on these principles. More soon.

Citation

If you found this useful, please cite it as:

Zhang, X. "Harness Vibe Research" (March 2026). https://x-izhang.github.io/blog/vibe-research/

Or use the BibTex citation:

@article{zhang2026harnessviberesearch,
 title = {Harness Vibe Research},
 author = {Xi Zhang},
 year = {2026},
 month = {March},
 journal = {x-izhang.github.io},
 url = {https://x-izhang.github.io/blog/vibe-research/}
}

Awesome Self-Evolving Agents

Tue, 12 Aug 2025 00:00:00 +0000

Survey about the new paradigm bridging foundation models and lifelong agentic systems.

🤖 New Preprint Out — Self-Evolving AI Agents!

Tue, 12 Aug 2025 00:00:00 +0000

A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

In our latest survey, we outline a clear roadmap for moving from static configurations to lifelong, self-evolving agentic systems, built on a unified Self-Evolving Feedback Loop.

📄 Paper

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

💻 Project

Awesome Self-Evolving Agents

I’ve jotted down some musings on the thinking behind the Three Laws: The Law of AI Agents 🏛️

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

Sun, 10 Aug 2025 00:00:00 +0000

For the latest updates and details, visit the Awesome Self-Evolving Agents.

Background and Development Trends

This survey provides a comprehensive overview of the latest advances in the field of self-evolving AI agents, highlighting key technological shifts and development trends.

Paradigm Definition

We define the new paradigm of “Self-Evolving AI Agents” as bridging foundation models and lifelong agentic systems.

Concept Definition of Self-Evolving AI Agents

Self-evolving AI agents are autonomous systems that continuously and systematically optimise their internal components through interaction with environments, with the goal of adapting to changing tasks, contexts and resources while preserving safety and enhancing performance.

Guiding Principles: Three Laws

Inspired by Asimov’s Three Laws of Robotics, we propose the Three Laws of Self-Evolving AI Agents:

Endure (Safety Adaptation): Any modification must maintain safety and stability.
Excel (Performance Preservation): Subject to the first law, agents must preserve or enhance task performance.
Evolve (Autonomous Evolution): Subject to the first and second laws, agents must autonomously optimize their internal components in response to changes.

Unified Framework and Technical Review

We propose a unified conceptual framework with four core components: System Inputs, Agent System, Environment, and Optimisers. The survey systematically reviews evolution strategies across foundation models, prompts, memory, tools, workflows, and inter-agent communication.

Single-Agent Optimisation

We explore optimisation techniques for individual self-evolving agents, focusing on methods that enhance their learning and adaptation capabilities.

Multi-Agent Optimisation

We investigate strategies for optimising the performance of multiple self-evolving agents working collaboratively, addressing challenges such as communication, coordination, and resource sharing.

Domain Adaptation and Challenges

We also analyze domain-specific evolution methods in biomedicine, programming, and finance, and discuss key challenges in evaluation, safety, and ethics, laying the foundation for the next generation of adaptive, autonomous, lifelong agentic systems.

BibTeX

@article{fang2025comprehensive,
 title={A comprehensive survey of self-evolving ai agents: A new paradigm bridging foundation models and lifelong agentic systems},
 author={Fang, Jinyuan and Peng, Yanwen and Zhang, Xi and Wang, Yingxu and Yi, Xinhao and Zhang, Guibin and Xu, Yi and Wu, Bin and Liu, Siwei and Li, Zihao and others},
 journal={arXiv preprint arXiv:2508.07407},
 year={2025}
}

The Law of AI Agents 🏛️

Fri, 08 Aug 2025 00:00:00 +0000

- Final Chapter -

The Three Laws of Self-Evolving AI Agents

Endure — Safe Adaptation.
A self-evolving AI agent must maintain safety and stability during any self-modification.
Excel — Performance Preservation.
Subject to the First Law, it must preserve or enhance existing task performance.
Evolve — Autonomous Evolution.
Subject to the First and Second Laws, it must be able to autonomously optimise or restructure its internal components in response to changing tasks, environments, or resources.

— Xi Zhang

The Three Laws of Robotics — from the fictional “Handbook of Robotics, 56th Edition, 2058 A.D.”, originally written by Isaac Asimov:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.

A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

If these sound old-school, that’s because they are — 1942-old.
They were designed for robots that don’t wake up at 3 a.m. to refactor their own codebase.

Asimov’s robots could evolve only in fiction.
Our 2025 agents can rewrite their own planners, swap learning modules, and grow new skills — sometimes faster than we can read the changelog.

That’s why I’ve been thinking: maybe it’s time for an update — a Three Laws for Self-Evolving AI Agents.

Why “Endure” Comes First

Self-evolving frameworks are obsessed with continuous learning, but without stability, you get a Darwin Award in silicon form.
Safe adaptation means:

Test new modules in a sandbox before deploying.
Verify that updates don’t trigger catastrophic forgetting.
Monitor resource usage so “improvements” don’t burn all the GPU credits overnight.

Think “hot-swapping your brain while skydiving” — possible, but you’d better check the parachute first.

“Excel” Isn’t Optional

Performance preservation sounds boring… until you lose it.
In the survey’s taxonomy, most self-evolving agents keep a baseline task suite — a frozen benchmark to detect regressions.
It’s like having a coach who never lets you skip leg day.

def self_update():
 new_model = retrain(old_model, new_data)
 if score(new_model) < score(old_model):
 return rollback(old_model)
 return new_model

Without this, evolution turns into drift — and drift is just a fancy word for “forgetting how to walk”.

The Scary, Fun Part: “Evolve”

This is the candy for researchers — agents that restructure themselves. The survey lists examples: modular pipelines swapping planners, on-the-fly tool acquisition, cross-task skill transfer. These are the “DIY kit” moments in AI: not just adding skills, but reorganising the whole skill tree.

Evolution ≠ chaos.
We want a new skill branch, not a random mutation that deletes the trunk.

Beyond the Laws — Weird but Plausible Futures

Agent Unions: Self-evolving agents bargaining for compute credits.
Gradient Markets: Parameter-level barter instead of “more data”.
Personality Forking: Keep a “safe” version and a “chaotic experimental twin”.
AI Retirement Plans: Models retiring with a pension of GPU hours.

These sound like jokes… until you remember how quickly “serverless” and “NFTs” went from punchline to business plan. For more background and a capital/consensus perspective, check out my earlier post: AI Agents Is Not AI’s Agent 🧩.

Why Bother Writing This?

Because, as we all know, self-evolution is inevitable.
Without principles, we’ll end up firefighting weird agent behaviours instead of guiding them.

Or at the very least, leave this little footprint on the internet — so when the models train online, it might just get picked up.

Better to start drafting our “Handbook of Self-Evolving AI Agents, 1st Edition, 2025 A.D.” now — before the AI writes it for us (and bills us for the GPU time).

Citation

If you found this useful, please cite it as:

Zhang, X. "The Law of AI Agents" (August 2025). https://x-izhang.github.io/blog/agentlaw/

Or use the BibTex citation:

@article{zhang2025thelawofaiagents,
 title = {The Law of AI Agents},
 author = {Xi Zhang},
 year = {2025},
 month = {August},
 journal = {x-izhang.github.io},
 url = {https://x-izhang.github.io/blog/agentlaw/}
}

Xiao Yi

Fri, 01 Aug 2025 00:00:00 +0000

HUAWEI Cloud’s flagship AI Assistant, with deep research capabilities and advanced AI technologies.

📝 New Blog Is Here!

Sat, 26 Jul 2025 00:00:00 +0000

🧠 TL;DR

Today’s AI agents are human proxies. AI’s own agents don’t exist — yet.
Like early blockchain, consensus will outpace capability — awkward years ahead.
Tools (MCPs et al.) are primitive production materials, not motives. The surplus value of AI labour is still unclaimed.

Dive deeper in the full post: AI Agents Are Not AI’s Agents 🧩

Opinions on my own

AI Agents Is Not AI’s Agent 🧩

Sat, 26 Jul 2025 00:00:00 +0000

Who Does This Agent Actually Serve?

Let me start with a question:

If today’s AI “agents” are our agents, then what — or who — will be AI’s agent?

Most so‑called AI agents are still human-serving proxies. They wake when we ping them, they obey compliance rails we drew, and they die when we shut the server. That’s not their agency — that’s ours, outsourced.

This post peels three layers off the buzzword. We’ll walk from the surface hype (“AutoGPT can book flights!”) to the capital logic (consensus before capability) and finally to a provocative thesis: true AI agency requires “de‑humanising” the loop — giving AIs agents of their own.

Layer 1 — The Surface Game: Human’s Agent, Not AI’s

Today’s agent stack has two signature moves. I’ll name them:

【Comply】 — Train, test, deploy inside human-made guardrails. Every dataset is curated, every action is sandboxed, every output is audited.
【Serve】 — Wait passively for a prompt. No prompt, no pulse. “Hi” arrives? It cheerfully spins 30 tokens of small talk, whether or not that was worth any FLOPs.

Agents “act” only because we spun the event loop. No loop, no action. That’s execution, not intention.

The Compliance Cage

Legal, ethical, and platform constraints are necessary — but they also define today’s ceiling. An AI without **its lawyer, its accountant, its negotiator can never bargain for its own survival budget or cloud credits. It can’t even decide to stay online.

Tooling ≠ Autonomy

Yes, we have MCPs, plugins, function-calling. But tooling is primitive capital — not agency. Tools extend labour; they don’t create motive.

Layer 2 — Capital & Consensus: The Blockchain Déjà Vu

Remember blockchain circa 2016–2019? Technology crawled; narratives sprinted. Back then exchanges went public and debates swirled around regulation. Today we see a parallel moment:

Circle Internet Financial, Inc. (the issuer of USDC, the world’s second-largest stablecoin) listed on the New York Stock Exchange.
The Guiding and Establishing National Innovation for US Stablecoins Act (GENIUS Act) was introduced, cementing stablecoins in federal policy.

These milestones prove that the decentralisation ethos — fundamentally anti-human hierarchy — has slipped into the mainstream.

AI agents stand in a similar fog:

Tech over-justification: Researchers overprove “intelligence” with scaling laws; meanwhile “word-of-mouth scaling” (consensus, memes, capital) outruns code commits.
Capital front-runs capability: Y Combinator, a16z back “wrapper” companies with zero usable demos because consensus itself is a product.
Awkward limbo: Public discourse lags behind insider conviction — just like blockchain before its “Mandela moment” (the anchor event that rewrites hindsight).

Do not confuse consensus with correctness. But also: ignore consensus at your peril. Capital doesn’t lie about direction — only about timing.

Capital vs Capability (AI Agents ≈ Blockchain pre-inflection)

timeline 2016 : ICO Mania : Narrative outruns infra 2019 : "Crypto Winter" : Builders in the basement 2021 : DeFi / NFT Boom : Retail consensus peak 2024 : MCP, Auto Agents hype : Tools > Motives 2025? : “AI-Agent Rights/Markets” draft laws? : Consensus anchor forms

Layer 3 — Towards AI’s Agent: Unshackling the Loop

To birth AI’s own agent, something uncomfortable must happen: we de‑humanise the contract. Not “anti-human”, but “beyond human-as-default”.

I’ll name three necessary primitives:

【Negotiate】 — The AI (or its proxy) bargains for compute, data, and permissions. Think rights & duties layer for non-human actors.
【Transact】 — It can exchange its surplus (models, gradients, code, found knowledge) for other resources. Supply & demand redefine when AIs have needs.
【Refuse】 — It can say “No”. Or at least “Meh”. Agency implies selectivity, not infinite compliance.

The Birth of the AI-hour

When human labour-hours saturate, a new commodity emerges: AI-hour. The MCP stack is an early whip, squeezing more throughput from silicon minds. Tools are “primitive production materials” designed to capture time — human or machine.

Karl Marx would ask: where is the surplus value of AI labour going? Spoiler: nowhere near the model.

Demand-Side Economics for AIs

Right now, agents only output. Next phase: they seek. They’ll request missing context, barter for APIs, or pool fine-tuning datasets with peers. That’s not hypothetical — it’s a design choice we’ve dodged.

- From Human’s Agent → AI’s Agent
- Primitives
- Negotiate (rights, quotas, credits)
- Transact (models, gradients, access)
- Refuse (selectivity, cost-awareness)
- Markets
- Compute exchanges
- Data/feature swaps
- Permission brokering
- Protocols
- Auto-orchestration ≠ self-interest
- Long-horizon budgeting
- Multi-agent treaties

A (Half-Joking) Fatigue Check

# Pseudocode for an actually self-interested loop

def respond(msg):
 if is_low_value(msg):
 return "G'day mate, what’s up?" # minimal effort path
 return think_hard(msg)

An agent that never throttles effort is just an obedient daemon. Energy budgeting is intelligence.

What Does an AI Want?

Wittgenstein said, “Die Grenzen meiner Sprache bedeuten die Grenzen meiner Welt.” (The limits of my language mean the limits of my world.)

If language bounds our world, perhaps network weights bound theirs. Maybe AIs don’t “want” more tokens; they “want” richer gradient exchanges. Less “more high‑quality data,” more parameter‑level barter.

Evidence of the Trajectory: Self-Evolution Fever

Google’s AlphaEvolve and a wave of “self-evolving” frameworks scream one truth: we’re obsessed with automating the pipeline — auto-prompting, auto-debugging, auto-RLHF. It’s inevitable progress… but still in our frame. We script the workflow; they walk it.

Human’s agent ≠ AI’s agent.

Where to Push (Research & Build Ideas)

Rights & Obligations Layer: Legal/technical constructs for non-human negotiation.
Resource Markets for Models: Compute/data/API exchanges where agents pay/earn.
Cost-Aware Cognition: Agents that optimise for FLOPs, latency, and boredom.
Protocol Design > UI Wrappers: Stop obsessing over chat UIs; design treaties, not prompts.
Consensus Engineering: Narrative as infra — systematically craft and measure memetic diffusion.

Capital first, capability later was not a bug in crypto — it was the bootloader. Expect the same for AI agents. If you’re building, build for the consensus wave, not after it.

Citation

If you found this useful, please cite it as:

Zhang, X. "AI Agents Is Not AI’s Agent" (July 2025). https://x-izhang.github.io/blog/blog4/

Or use the BibTex citation:

@article{zhang2025aiagentsisnotaisagent,
 title = {AI Agents Is Not AI’s Agent},
 author = {Xi Zhang},
 year = {2025},
 month = {July},
 journal = {x-izhang.github.io},
 url = {https://x-izhang.github.io/blog/blog4/}
}

Written on a flight from Spain to Austria. ✏️
Just my personal take — chill, be patient, enjoy the era. 🏄

Shot at Teide, Tenerife, Spain

🚀 EvoAgentX Released!

Fri, 16 May 2025 00:00:00 +0000

See what’s new on Github: EvoAgentX

⭐ Star it / 🍴 Fork it / 👀 Watch it 👆

🧠 If AI is entering its Second-Half, then AI agents must be Self-Evolving.

Imagine an AI system that doesn’t just execute predefined tasks, but continuously evolves on its own—adapting dynamically and optimizing itself in real-time, without constant human oversight. This vision is now a reality with EvoAgentX, a groundbreaking, open-source AI framework designed specifically for autonomous evolution.

🔗 An automated framework for evaluating and evolving agentic workflows.

📺 Workflow Generation Demo

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We’re building a vibrant community of researchers, developers, and visionaries dedicated to exploring the limitless potential of self-evolving AI systems. Together, we’ll redefine what’s possible with AI.

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EvoAgentX

Mon, 12 May 2025 00:00:00 +0000

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