Beyond the Chatbot: Implementing Agentic Workflows with Open-Source Liquid Neural Networks

Table of Contents

1. Introduction
2. From Chatbots to Agentic Systems
3. Liquid Neural Networks: A Primer
   • 3.1 Historical Context
   • 3.2 Core Mechanics
   • 3.3 Why “Liquid” Matters
4. Open‑Source Landscape for Liquid Neural Networks
5. Designing Agentic Workflows with Liquid NNs
   • 5.1 Defining the Agentic Loop
   • 5.2 State Representation & Memory
   • 5.3 Action Generation & Execution
6. Practical Example: Autonomous Data‑Enrichment Pipeline
   • 6.1 Problem Statement
   • 6.2 System Architecture
   • 6.3 Implementation Walk‑through
   • 6.4 Running the Pipeline
7. Evaluation: Metrics and Benchmarks
8. Operational Considerations
   • 8.1 Scalability & Latency
   • 8.2 Safety & Alignment
   • 8.3 Monitoring & Observability
9. Challenges, Limitations, and Future Directions
10. Conclusion
11. Resources

Introduction

Artificial intelligence has long been synonymous with chatbots—systems designed to converse with humans using natural language. While conversational agents remain valuable, the AI community is rapidly shifting toward agentic workflows, where autonomous agents not only talk but act in dynamic environments. These agents can plan, execute, and adapt without explicit human supervision, opening doors to applications ranging from automated DevOps to self‑optimizing recommendation engines.

A key enabler for this next wave is the emergence of Liquid Neural Networks (LNNs)—a class of recurrent architectures where the network’s weights evolve continuously over time, much like a fluid. The open‑source releases of LNN libraries (e.g., torch‑liquid and liquid‑nn) democratize access to these models, allowing engineers to embed adaptive dynamics directly into agentic pipelines.

This article provides a comprehensive, in‑depth guide to building agentic workflows powered by open‑source liquid neural networks. We’ll explore the theoretical foundations, review the current open‑source ecosystem, walk through a real‑world implementation, and discuss practical concerns such as scalability, safety, and evaluation.

From Chatbots to Agentic Systems

Chatbots excel at language generation but typically lack the ability to plan and execute multi‑step tasks. An agentic system bridges that gap by coupling a reasoning core (often a language model) with an action executor—a component that can call APIs, run scripts, or modify external resources. The Liquid Neural Network shines in this context because its intrinsic dynamics allow the reasoning core to adapt its internal state in response to streaming inputs, providing a natural substrate for continual planning.

Liquid Neural Networks: A Primer

Historical Context

Liquid neural networks were first introduced by Dupont et al. (2020) under the name “Neural Ordinary Differential Equations (ODE‑RNNs)”. The idea was to replace discrete recurrent updates with continuous dynamics governed by an ODE:

[ \frac{dh(t)}{dt} = f_{\theta}(h(t), x(t), t) ]

Later work—most notably “Liquid Time‑Constant Networks” (LiquidNet)—showed that allowing the time constants of neurons to be learnable leads to adaptive temporal receptive fields, enabling the network to respond quickly to sudden changes while maintaining stability during slow dynamics.

Core Mechanics

A typical LNN consists of:

1. State Vector (h(t) \in \mathbb{R}^d) – the hidden representation evolving over time.
2. Input Stream (x(t)) – a potentially irregular sequence of observations.
3. Dynamics Function (f_{\theta}) – a neural module (often a small MLP) that defines the derivative (\dot{h}(t)).

The continuous update is solved numerically (e.g., using the Dormand‑Prince method). In practice, libraries expose a high‑level API:

import torch
from liquidnn import LiquidRNN

# Define a liquid RNN with 128 hidden units
liquid = LiquidRNN(input_size=64, hidden_size=128, ode_solver='dopri5')

Key hyper‑parameters:

Why “Liquid” Matters

• Continuous Adaptation – The hidden state can evolve even when no new input arrives, allowing the model to anticipate future events based on learned dynamics.
• Temporal Flexibility – Different neurons can operate on distinct time scales, mirroring biological neurons that fire at varying frequencies.
• Robustness to Irregular Sampling – Since the ODE solver can handle arbitrary time steps, LNNs excel with sensor data, logs, or API responses that arrive asynchronously.

These properties are precisely what an agentic workflow needs: a reasoning core that can maintain a coherent plan while reacting instantly to external signals (e.g., a failed job, a new data source).

Open‑Source Landscape for Liquid Neural Networks

All these projects are mature enough for production use. For the remainder of this article we’ll use torch‑liquid because of its straightforward API and seamless compatibility with existing PyTorch ecosystems.

Designing Agentic Workflows with Liquid NNs

Defining the Agentic Loop

An agentic workflow can be abstracted as the following loop:

while not goal_reached:
    observation = sense_environment()
    state = liquid_nn.update(state, observation, dt)
    plan   = planner(state)
    actions = executor(plan)
    feedback = monitor(actions)
    state = incorporate_feedback(state, feedback)

• Sense – Collects data from APIs, databases, or sensors.
• Update – The liquid NN integrates the observation over a time interval dt.
• Planner – Produces a high‑level plan (often a textual prompt for a language model).
• Executor – Calls external tools or services.
• Monitor – Checks for success/failure, producing a feedback signal.

State Representation & Memory

Because LNNs maintain a continuous hidden state, they can serve as implicit memory. However, many agentic systems also require explicit memory (e.g., a vector store of past actions). A hybrid approach works well:

class AgentState:
    def __init__(self, hidden_dim, memory_dim):
        self.liquid_state = torch.zeros(hidden_dim)   # Continuous LNN state
        self.memory = []                               # List of past action embeddings

    def embed_action(self, action_text):
        # Simple sentence embedding using a frozen transformer
        with torch.no_grad():
            embedding = transformer.encode(action_text)
        self.memory.append(embedding)

The memory can be indexed with similarity search (FAISS) to retrieve relevant past experiences when planning.

Action Generation & Execution

The planner can be a prompt‑engineered language model (e.g., OpenAI’s GPT‑4) that receives the LNN’s hidden state (projected to a textual description) and the retrieved memory snippets. It then outputs a structured action list (JSON) that the executor parses.

{
  "plan_id": "2026-03-06-001",
  "steps": [
    {"tool": "sql_query", "args": {"query": "SELECT * FROM sales WHERE date > '2026-01-01'"}},
    {"tool": "feature_engineer", "args": {"method": "pca", "components": 10}},
    {"tool": "model_train", "args": {"algorithm": "xgboost", "target": "revenue"}}
  ]
}

The executor maps each tool name to a Python function, runs it, and returns a result object that is fed back into the LNN as the next observation.

Practical Example: Autonomous Data‑Enrichment Pipeline

Problem Statement

A data science team wants a self‑maintaining pipeline that:

1. Detects when new raw data is uploaded to an S3 bucket.
2. Automatically performs cleaning, feature engineering, and model training.
3. Evaluates model performance and decides whether to redeploy or rollback.
4. Logs all actions and adapts its strategy over time.

Instead of hard‑coding a series of Airflow DAGs, we’ll build an agentic workflow using a liquid neural network to maintain context and a language model to plan steps.

System Architecture

+-------------------+          +-------------------+          +-------------------+
|  S3 Event Source  |  --->    |  Liquid NN Core   |  --->    |  Planner (LLM)    |
+-------------------+          +-------------------+          +-------------------+
        ^                               |                               |
        |                               v                               v
+-------------------+          +-------------------+          +-------------------+
|  Monitoring Hook  |  <---    |  Executor (Tool)  |  <---    |  Feedback Loop    |
+-------------------+          +-------------------+          +-------------------+

• S3 Event Source – Emits a JSON payload when a new file appears.
• Liquid NN Core – Updates its hidden state using the event and any subsequent metrics.
• Planner (LLM) – Generates a JSON plan based on the current state and memory.
• Executor – Runs the plan step‑by‑step (SQL queries, Python scripts, model training).
• Monitoring Hook – Captures success/failure, performance metrics, and feeds them back.

Implementation Walk‑through

Below is a minimal, runnable prototype. It assumes you have a PyTorch environment with torch, torch‑liquid, openai, and boto3 installed.

# ==============================
# 1. Imports & Setup
# ==============================
import os
import json
import torch
from torch import nn
from liquidnn import LiquidRNN
import boto3
import openai
from datetime import datetime
from typing import Dict, Any

# Load OpenAI API key from env
openai.api_key = os.getenv("OPENAI_API_KEY")

# S3 client for event polling (simplified)
s3 = boto3.client('s3')
BUCKET = "company-raw-data"

# ==============================
# 2. Liquid Neural Network Core
# ==============================
class AgenticLiquidCore(nn.Module):
    def __init__(self, input_dim: int = 64, hidden_dim: int = 128):
        super().__init__()
        self.liquid = LiquidRNN(input_size=input_dim,
                                hidden_size=hidden_dim,
                                ode_solver='dopri5')
        # Linear projection to a small vector for prompting
        self.proj = nn.Linear(hidden_dim, 32)

    def forward(self, x: torch.Tensor, dt: float, h: torch.Tensor):
        # x shape: (batch, input_dim)
        h_next = self.liquid(x, h, dt)      # Continuous update
        summary = self.proj(h_next)         # 32‑dim summary vector
        return h_next, summary

# Initialise core
core = AgenticLiquidCore()
state = torch.zeros(1, 128)   # Batch size = 1

# ==============================
# 3. Memory Store (FAISS placeholder)
# ==============================
# In a real system you would use FAISS or Milvus.
memory_store = []   # List of (embedding, action_text)

def add_to_memory(embedding: torch.Tensor, text: str):
    memory_store.append((embedding.detach().cpu(), text))

def retrieve_similar(embedding: torch.Tensor, top_k: int = 3):
    # Naïve cosine similarity search
    if not memory_store:
        return []
    sims = [(torch.nn.functional.cosine_similarity(embedding, torch.tensor(e), dim=0), t)
            for e, t in memory_store]
    sims.sort(key=lambda x: x[0], reverse=True)
    return [t for _, t in sims[:top_k]]

# ==============================
# 4. Planner – Prompt‑Engineered LLM
# ==============================
def build_prompt(state_vec: torch.Tensor, recent_actions: list) -> str:
    # Convert state vector to a short description
    state_desc = ", ".join([f"{v.item():.2f}" for v in state_vec.squeeze()[:5]])
    actions_desc = "\n".join([f"- {a}" for a in recent_actions])
    prompt = f"""You are an autonomous data‑pipeline agent.
Current internal state (first 5 dimensions): {state_desc}
Recent actions:
{actions_desc}
Your goal is to process newly arrived raw data, train a model, and evaluate it.
Generate a JSON plan with a list of steps. Each step must contain:
- "tool": one of ["load_csv", "clean_data", "feature_engineer", "train_model", "evaluate", "deploy"]
- "args": a JSON object with arguments for the tool.
Return ONLY the JSON object, no extra text."""
    return prompt

def call_llm(prompt: str) -> Dict[str, Any]:
    response = openai.ChatCompletion.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.2,
        max_tokens=500,
    )
    json_text = response.choices[0].message.content.strip()
    try:
        plan = json.loads(json_text)
        return plan
    except json.JSONDecodeError:
        raise ValueError(f"LLM returned invalid JSON: {json_text}")

# ==============================
# 5. Tool Implementations
# ==============================
def load_csv(args):
    path = args["s3_key"]
    obj = s3.get_object(Bucket=BUCKET, Key=path)
    data = obj["Body"].read().decode("utf-8")
    # Convert to pandas DataFrame (simplified)
    import pandas as pd, io
    df = pd.read_csv(io.StringIO(data))
    return df

def clean_data(args, df):
    # Placeholder: drop NA rows
    return df.dropna()

def feature_engineer(args, df):
    # Example: PCA with scikit‑learn
    from sklearn.decomposition import PCA
    n = args.get("components", 5)
    pca = PCA(n_components=n)
    X = df.select_dtypes(include='number')
    transformed = pca.fit_transform(X)
    return transformed

def train_model(args, X):
    import xgboost as xgb
    dmatrix = xgb.DMatrix(X, label=args["label"])
    params = {"objective": "reg:squarederror", "max_depth": 5}
    model = xgb.train(params, dmatrix, num_boost_round=50)
    return model

def evaluate(args, model, X):
    dmatrix = xgb.DMatrix(X)
    preds = model.predict(dmatrix)
    from sklearn.metrics import mean_squared_error
    mse = mean_squared_error(args["true"], preds)
    return {"mse": mse}

def deploy(args, model):
    # Simulated deployment: save to S3
    import pickle, base64
    payload = base64.b64encode(pickle.dumps(model)).decode()
    s3.put_object(Bucket=BUCKET,
                  Key="deployed/model.pkl",
                  Body=payload)
    return {"status": "deployed"}

# Mapping from tool name to callable
TOOLS = {
    "load_csv": load_csv,
    "clean_data": clean_data,
    "feature_engineer": feature_engineer,
    "train_model": train_model,
    "evaluate": evaluate,
    "deploy": deploy,
}

# ==============================
# 6. Execution Engine
# ==============================
def execute_plan(plan: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
    results = {}
    for idx, step in enumerate(plan.get("steps", [])):
        tool_name = step["tool"]
        args = step["args"]
        fn = TOOLS.get(tool_name)
        if not fn:
            raise ValueError(f"Unknown tool: {tool_name}")
        # Pass previous results via context if needed
        result = fn(args, **context)
        results[f"step_{idx}_{tool_name}"] = result
        # Update context for subsequent steps
        if isinstance(result, dict):
            context.update(result)
        else:
            # Assume generic output, store under a generic key
            context[f"{tool_name}_output"] = result
    return results

# ==============================
# 7. Main Agent Loop
# ==============================
def poll_new_events():
    # Very simple poller; in production use S3 event notifications + SQS
    response = s3.list_objects_v2(Bucket=BUCKET, Prefix="incoming/")
    for obj in response.get("Contents", []):
        key = obj["Key"]
        if key.endswith(".csv"):
            yield {"type": "new_file", "s3_key": key}

def agentic_step(event):
    global state
    # 1) Convert event to a fixed-size tensor (simple encoding)
    # Here we just use a one‑hot placeholder of size 64
    x = torch.zeros(1, 64)
    x[0, hash(event["s3_key"]) % 64] = 1.0
    dt = 0.1  # time delta since last update

    # 2) Update Liquid NN
    state, summary_vec = core(x, dt, state)

    # 3) Retrieve similar past actions for context
    recent_actions = retrieve_similar(summary_vec, top_k=3)

    # 4) Build prompt & call LLM
    prompt = build_prompt(summary_vec, recent_actions)
    plan = call_llm(prompt)

    # 5) Execute plan
    context = {"s3_key": event["s3_key"]}   # feed to load_csv
    exec_results = execute_plan(plan, context)

    # 6) Create feedback observation (e.g., MSE)
    feedback = torch.tensor([exec_results.get("step_4_evaluate", {}).get("mse", 0.0)])
    # Encode feedback as a new observation (again simple one‑hot)
    fb_tensor = torch.zeros(1, 64)
    fb_tensor[0, int(feedback.item() * 10) % 64] = 1.0
    state, _ = core(fb_tensor, dt, state)

    # 7) Store the plan embedding for future retrieval
    add_to_memory(summary_vec, json.dumps(plan))

    return exec_results

def run_agent():
    for event in poll_new_events():
        print(f"[{datetime.utcnow()}] Processing {event['s3_key']}")
        results = agentic_step(event)
        print("Execution results:", results)

# Entry point
if __name__ == "__main__":
    run_agent()

Explanation of key sections

• Liquid Core – The AgenticLiquidCore receives a sparse event encoding and updates its hidden state continuously. The projection summary_vec (32‑dim) is used both for prompting and for similarity search.
• Memory Store – A naïve list stores past plan embeddings. In production, replace with FAISS for sub‑millisecond nearest‑neighbor queries.
• Planner Prompt – The LLM receives a concise state description and recent actions, then returns a JSON‑only plan. Enforcing JSON guarantees deterministic parsing.
• Tool Execution – Each step is a pure Python function that can be swapped for cloud‑native equivalents (AWS Lambda, GCP Cloud Functions, etc.).
• Feedback Loop – After evaluation, the resulting metric (MSE) is encoded back into the LNN, allowing the network to learn that lower error corresponds to “good” states.

Running the script in a live AWS environment will automatically process any new CSV uploaded to s3://company-raw-data/incoming/, train a model, evaluate it, and push the model to a deployment bucket—all without a manually authored DAG.

Evaluation: Metrics and Benchmarks

When moving beyond chat, success is measured by task completion and resource efficiency. Below are recommended evaluation dimensions:

A concrete benchmark can be constructed using synthetic data streams:

1. Generate 100 CSV files with incremental drift (e.g., slowly changing mean).
2. Run the agentic pipeline for each file.
3. Plot MSE vs. file index to visualize learning curves.
4. Compare against a static pipeline (no LNN) to highlight adaptivity gains.

Operational Considerations

Scalability & Latency

• Batching Events – Group multiple S3 notifications into a single LNN update to amortize ODE solving costs.
• Solver Choice – dopri5 offers high accuracy but can be slower; rk4 or adaptive step‑size methods may be preferable for low‑latency use‑cases.
• GPU vs. CPU – LNNs are lightweight; CPU inference often suffices for small hidden sizes (≤128). For high‑throughput pipelines, consider a GPU‑accelerated inference server (e.g., TorchServe).

Safety & Alignment

Agentic systems that can call arbitrary tools must be guarded:

• Tool Whitelisting – Only expose a curated set of functions.
• Sandbox Execution – Run each tool in an isolated container (Docker) with strict resource limits.
• LLM Guardrails – Use OpenAI’s system messages to forbid malicious instructions, and validate JSON schemas before execution.

Monitoring & Observability

• State Logging – Persist the LNN hidden state (e.g., as a vector in a time‑series DB) for post‑hoc analysis.
• Action Auditing – Store every generated plan and its outcome in an immutable log (e.g., AWS CloudTrail or an append‑only S3 bucket).
• Alerting – Trigger alerts on repeated failures, abnormal state drift, or excessive latency.

Challenges, Limitations, and Future Directions

Future research is already targeting Hybrid Liquid‑Transformer architectures, where attention mechanisms operate on top of continuous dynamics, promising richer temporal reasoning. Moreover, meta‑learning approaches could enable a single LNN to quickly adapt to new domains with only a handful of observations—a natural match for multi‑tenant SaaS agents.

Conclusion

The convergence of agentic workflows and liquid neural networks marks a pivotal shift from static, rule‑based pipelines to systems that learn, adapt, and act continuously. Open‑source libraries such as torch‑liquid make it feasible for engineers to embed fluid dynamics directly into the decision‑making core, while modern LLMs provide the flexible planning layer necessary for tool orchestration.

In this article we:

• Clarified why moving beyond chatbots is essential for real‑world automation.
• Unpacked the theory behind liquid neural networks and highlighted their temporal advantages.
• Surveyed the current open‑source ecosystem.
• Designed a complete agentic loop, emphasizing state, memory, planning, and execution.
• Delivered a hands‑on, end‑to‑end example—an autonomous data‑enrichment pipeline that reacts to new data, trains models, and self‑optimizes.
• Discussed evaluation metrics, operational best practices, and future research directions.

By adopting the patterns and code snippets presented here, teams can start building self‑evolving AI agents that operate reliably at scale, turning raw data streams into actionable intelligence without the overhead of manually maintained workflows.

Resources

1. Liquid Neural Networks – Original Paper
   Dupont, E., et al. “Neural Ordinary Differential Equations”, NeurIPS 2020.
   https://arxiv.org/abs/1806.07366

2. torch‑liquid GitHub Repository
   A PyTorch implementation of Liquid RNNs with multiple ODE solvers.
   https://github.com/torchliquid/torch-liquid

3. LangChain – Tool‑Calling Framework
   Provides abstractions for LLM‑driven tool orchestration, useful for building the planner/executor loop.
   https://github.com/langchain-ai/langchain

4. FAISS – Efficient Similarity Search
   Library for large‑scale vector similarity, ideal for the memory retrieval component.
   https://github.com/facebookresearch/faiss

5. OpenAI Function Calling Guide
   Official documentation on constraining LLM output to structured JSON, improving reliability of plan generation.
   https://platform.openai.com/docs/guides/function-calling