Tencent HY4 Review: Building a Reliable AI Agent for Real Work

TL;DR

• Tencent HY4 Preview proves that a modern ai agent can handle long-horizon tasks without losing context or hallucinating.
• Tool orchestration is shifting from manual prompting to autonomous selection, which allows agents to seamlessly move between browsers, skills, and terminals.
• New frameworks like State-Aware Runtime and Claworc are finally solving the persistent memory and security issues that plague complex agentic workflows.
• When managing raw business data, an advanced ai agent can now manipulate structured files while maintaining absolute numerical accuracy.

I got early access to Tencent's HY4 Preview and wanted to test it beyond the usual "ask a question and get an answer" workflow. Because evaluating an ai agent today requires more than simple chat prompts, I built three practical tests around areas where these models usually struggle: long-horizon planning, multi-tool execution, and productivity/data analysis.

The goal wasn't to benchmark it with synthetic questions. I wanted to see whether it could actually complete a task, maintain context, use tools correctly, and produce something usable. Hy4 preview is a new-generation Mixture-of-Experts (MoE) flagship model featuring 770B total parameters and a massive 1M context window. Before diving into the tests, I spun up the model using the official vLLM Docker image from the Tencent Hy4-preview GitHub repository.

docker run --gpus all \
  -p 8000:8000 \
  --ipc=host \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:hy4-preview tencent/Hy4-preview-FP8 \
    --tensor-parallel-size 8 \
    --attention-backend FLASHMLA_SPARSE \
    --tool-call-parser hy_v4

Once the model was running, I handed over control to the ai agent to see how much work it could figure out on its own.

01. Intelligent Agent Planning - Building a Lead List

The use case

The first test was deliberately open-ended. I asked HY4 to build a lead list by researching LinkedIn and multiple sources across the web. The important part was that I didn't break the task into individual prompts like:

Search LinkedIn → find companies → find people → verify them → create a list.

Instead, I gave it the objective and let the model figure out the sequence.

The workflow

Goal
  ↓
Understand requirements
  ↓
Search LinkedIn
  ↓
Find relevant companies / people
  ↓
Cross-check web sources
  ↓
Extract useful information
  ↓
Filter + organize
  ↓
Final lead list

The interesting part was the state maintained between steps. It didn't treat every search as an isolated question. The information discovered earlier continued to influence what it searched for and what it kept.

A simplified implementation

The exact internal workflow isn't something I exposed, but conceptually, an ai agent doing this could look like:

task = """
Build a lead list of AI companies.
Find relevant decision-makers,
verify their information using multiple sources,
and return a structured table.
"""

state = {
    "goal": task,
    "leads": [],
    "sources": []
}

while not task_complete(state):

    next_action = model.plan(state)

    result = tools.execute(
        tool=next_action.tool,
        arguments=next_action.arguments
    )

    state = model.update_state(
        state=state,
        result=result
    )

final_list = model.format(
    state["leads"],
    columns=[
        "name",
        "company",
        "role",
        "source"
    ]
)

The key idea isn't the loop itself. It's that the model maintains a working state instead of starting from zero after every action.

What I got

HY4 carried the workflow from research to the final list while keeping the original objective in context. What stood out to me was the lack of constant intervention. I wasn't repeatedly telling it what to search next or reminding it what the final output should look like. For a long-horizon task, that matters more to me than simply getting a good answer to one prompt.

How does an ai agent manage long-horizon state?

Handling extended workflows requires a reliable system layer. According to recent research on State-Aware Runtime, long-horizon failures usually happen because of unstable state maintenance and protocol drift rather than single-turn reasoning errors. Without strict boundaries, an unmonitored model can easily suffer from goal drift, which is exactly why we sometimes read bizarre reports of ai agents contacting consciousness researchers or hallucinating entirely new objectives.

To solve this, frameworks like LongHorizon-Harness introduce a Manage-Execute-Audit (MEA) loop. This design separates the active execution context from the canonical task state, which ensures that only independently verified facts are committed to memory. Similarly, methods explored in Beyond Semantic Organization actively isolate flawed execution branches so that intermediate errors do not corrupt the entire trajectory.

02. AI Agent Tool Orchestration - Browser to Terminal

This was probably the more interesting test from an engineering perspective.

The use case

I gave HY4 a task that required multiple tools to work together. The workflow involved researching information online, extracting relevant data, using a predefined skill, moving the result into the terminal, and generating a working script. Instead of manually switching between tools, I let the ai agent decide what it needed at each stage.

The workflow

              ┌─────────────┐
              │   Browser   │
              │   Research  │
              └──────┬──────┘
                     ↓
              ┌─────────────┐
              │    Skill    │
              │ Process data│
              └──────┬──────┘
                     ↓
              ┌─────────────┐
              │  Terminal   │
              │ Write / run │
              │   script    │
              └──────┬──────┘
                     ↓
              ┌─────────────┐
              │   Output    │
              │ Working code│
              └─────────────┘

The interesting part wasn't simply having access to three tools. It was tool selection and sequencing.

A simplified agent implementation

A basic version of this architecture could look like:

tools = {
    "browser": browser_tool,
    "skill": research_skill,
    "terminal": terminal_tool
}

state = {
    "objective": task,
    "research": None,
    "processed_data": None,
    "script": None
}

while not state["script"]:

    action = model.choose_tool(
        objective=state["objective"],
        state=state,
        available_tools=list(tools)
    )

    result = tools[action.name].run(action.arguments)

    state = model.update_state(
        state,
        result
    )

script = state["script"]

terminal_tool.run({
    "command": f"python generated_script.py"
})

In a production system, I'd add permission checks, structured tool schemas, retries, validation and execution sandboxes. But the architecture is essentially: Model → Tool selection → Tool execution → State update → Next action.

What I got

HY4 selected and chained the tools without me manually specifying every step. More importantly, the information extracted during the research stage remained useful when the task moved into the coding stage. That makes the difference between a model that can call tools and one that can actually orchestrate a workflow.

Why is tool sequencing difficult for an intelligent agent?

Tool sequencing becomes fragile when a model loses the exact syntax or context required for a specific CLI or browser interaction. To prevent this, developers are building specialized infrastructure. For example, browser-use/terminal is a Rust TUI that provides real Chrome sessions and explicit CDP control to the ai agent. You can hand the assistant raw browser capability with a simple bash command:

browser-use-terminal browser exec <<'PY'
new_tab("https://example.com")
wait_for_load()
print(capture_screenshot())
PY

When workflows move into the terminal, security becomes critical. Tools like Claworc act as an open-source orchestrator that gives every AI worker its own isolated workspace, complete with a live browser and persistent storage. This isolation is mandatory when ai agents executing unowned code need to interact with local file systems, because it ensures that experimental scripts cannot damage the host machine.

03. Data and Business Productivity - Turning Analytics Into Decisions

For the third test, I moved away from research and coding. I wanted to see how HY4 handled structured business data.

The use case

I took numbers from my Instagram analytics and brought them into Excel / Google Sheets. Instead of asking for a simple summary, I wanted a granular breakdown of the data. For example:

Post
 ├── Reach
 ├── Impressions
 ├── Likes
 ├── Comments
 ├── Shares
 ├── Saves
 └── Engagement Rate

The challenge here is different from web research. There is an existing source of truth, so the ai agent shouldn't invent values or silently modify the dataset.

A simple Python version

A similar analysis can be reproduced with pandas:

import pandas as pd

df = pd.read_excel("instagram_analytics.xlsx")

df["engagement"] = (
    df["likes"]
    + df["comments"]
    + df["shares"]
    + df["saves"]
)

df["engagement_rate"] = (
    df["engagement"] / df["reach"]
) * 100

top_posts = (
    df.sort_values(
        "engagement_rate",
        ascending=False
    )
    .head(10)
)

print(top_posts[
    [
        "post",
        "reach",
        "engagement",
        "engagement_rate"
    ]
])

From there, an agent can go further:

summary = {
    "total_posts": len(df),
    "avg_reach": df["reach"].mean(),
    "avg_engagement": df["engagement"].mean(),
    "best_post": top_posts.iloc[0]["post"],
    "best_rate": top_posts.iloc[0]["engagement_rate"]
}

print(summary)

The important thing is that the calculations should always originate from the source dataset rather than being generated as free-form text.

What I got

HY4 broke the analytics down to a fairly granular level while keeping the underlying numbers consistent. That was the main thing I was watching for. Zero invented numbers, zero unnecessary assumptions, and absolute preservation of the original values while moving between different calculations. For productivity tasks, that kind of reliability is arguably more useful than simply producing a polished summary.

Can an ai agent handle raw business data?

Yes, provided it operates within a constrained environment. Platforms like MagesticAI are proving that multi-agent orchestration can securely handle business logic. By utilizing a web-based UI that combines a Monaco code editor, PTY terminal access, and Graphiti cross-session memory, teams can trust an ai agent to review code, parse spreadsheets, and maintain strict data fidelity across multiple sessions.

Business Scale - Handling Data like the FIFA World Cup 2026™

Real-world business applications eventually demand massive scale. When global events capture the market's attention, the volume of live analytics and operational tasks explodes. Consider the upcoming FIFA World Cup 2026™ - businesses will need to parse millions of live data points, track shifting logistics, and update marketing campaigns in real time.

A traditional LLM would quickly succumb to context rot under that pressure. However, because HY4 utilizes a Gated DeepSeek Sparse Attention architecture with a 1M context length, it has the capacity to maintain deep state awareness. When paired with resilient orchestrators like Claworc and the MEA loops found in LongHorizon-Harness, an ai agent can dynamically adjust to high-velocity data environments without losing sight of the core objective.

What These 3 Tests Actually Showed Me

The three tests were intentionally different.

The common thread was execution. A normal LLM interaction often looks like:

Prompt → Response

The workflows I tested looked more like:

Goal
 ↓
Plan
 ↓
Observe
 ↓
Use tools
 ↓
Update context
 ↓
Verify
 ↓
Execute
 ↓
Result

That's the direction I find interesting with models like HY4. The question isn't only “How good is the model at answering?” It's increasingly:

“How much of the work can I give it and let it figure out?”

And these three tests gave me a pretty interesting answer. To summarize how the supporting infrastructure makes this possible, here is a breakdown of the tools that empower these long-horizon executions:

As these infrastructure layers mature, handing a massive project over to an ai agent is no longer science fiction. It is rapidly becoming standard engineering practice.

Frequently Asked Questions