From Chatbots to Autonomous Agents: How to Unlock Enterprise‑Wide Value

Written by Gerald Hewes

We already explained in a prior article The Future of Fintech: From AI Tools to Agentic Autopilot why agentic AI matters for today’s real‑estate and fintech operations. Now it’s time to build it!

Most organizations have deployed simple chatbots to answer FAQs or qualify leads. While this represents a foundational step, the true potential of AI emerges when systems evolve into autonomous agents and agentic systems. These entities pursue objectives with minimal human intervention, learn from interactions, and orchestrate complex workflows under robust safety and governance controls.

This post walks you through some of the critical technical foundations — memory persistence, real‑time observability, and robust safety nets — that enable the transition toward an agentic future.

From Reactive to Proactive: Understanding the Landscape

To set a baseline, let’s compare the core AI paradigms:

Paradigm	Autonomy Level	Key Features	Use Cases
Traditional AI	None	Static, manually trained models created by data scientists	Fraud detection, credit scoring, predictive maintenance, recommendation engines
Chatbots	Low	Respond to user inputs	Basic inquiry handling (e.g., “What’s my account balance?”)
AI Assistants	Medium	Task automation under user guidance	Analysts receiving data-driven insights; coding/data-analysis assistants
AI Agents	High	Perceive data, plan strategies, act autonomously, learn from feedback in a narrow domain	Real-time fraud detection, risk assessment, automated alerts
Agentic Systems	Very high	Orchestrate multiple sub-agents toward complex goals	Real-estate valuation systems where sub-agents aggregate data, analyze comps, and generate reports

Perceive‑Plan‑Act‑Learn Cycle

At their core, true AI agents and agentic systems follow a Perceive → Plan → Act → Learn (PPAL) cycle, continuously ingesting data, making decisions, executing actions, and feeding outcomes back into their models.

Stage	Definition	Agentic Example: Fraud Detection
Perceive	The agent gathers raw data from multiple sources (APIs, databases, real-time feeds).	An agent ingests real-time transaction logs, external risk feeds, and user-device telemetry via a secure API gateway and flags potentially risky transactions.
Plan	The agent reasons about the perceptual state to decide on a course of action and execute strategies to achieve specific objectives.	The agent applies multiple models to a flagged transaction, assigns a fraud score based on current activity and user history, and, if the score exceeds a dynamic threshold, triggers a security freeze on the transaction.
Act	The agent initiates actions across various systems.	The agent calls the bank’s ACH API to place an instant hold on the transfer and trigger a verification flow.
Learn	The agent continuously improves performance based on outcomes and feedback.	After the case is resolved (customer confirms or denies fraud), a separate agent retrains the model on the labeled outcome.

Case Study

Danske Bank reported a 60 % reduction in false positives after deploying an agentic fraud‑detection solution, with expectations to reach 80 % once further refinements are applied (Source: ThinkBig Analytics Case Study).

The Challenges of Scaling to Autonomous Agents

Scaling from scripted chatbots to autonomous agents demands a fundamental rethinking of infrastructure. At Proxet, we frequently see enterprises underestimate these specific hurdles:

Challenge	Why it Matters	Key Risks	Potential Solution(s)
Data Access & Governance	Lack of a unified view hinders insights; Access to external data; Data governance and privacy	Inconsistent or out-of-date data; Regulatory breaches	Unified catalog (e.g., Databricks Unity Catalog); Federated query engines (Snowflake, BigQuery); API-first data pipelines; Quality monitoring and compliance controls; Policy as code
Orchestration & Workflow Management	Coordinate actions and flows of specialized sub-agents	Deadlocks; Inefficiency and cost	Workflow engines (e.g., Temporal, LangGraph, Akka); State graphs and sub-agents
Memory Infrastructure	Stateless agents lose context, leading to inconsistent user experience; No “learnings” from corrections	Forced repetition by users; Low productivity, higher errors	Agent-framework long-term memory features; Standalone agentic memory frameworks
Observability & Continuous Monitoring	Silent failures go undetected; Difficult debugging and root-cause analysis; inability to optimize and improve	Security, compliance, and cost risks	AI Observability Platforms; Alerting on drift and latency; Manual and automated review & auditing (accuracy, quality, security, compliance)
Responsible AI	Bias in models → regulatory risk; Lack of transparency erodes trust	Regulatory paperwork or fines	Auditing tools (IBM AI Fairness 360); Explainability APIs (LIME, SHAP)

Deep Dive: A Technical Look

Memory Infrastructure: The Backbone of Adaptive Agents

“In agentic systems, long‑term memory isn’t just storage — it’s the foundation of true autonomy. Without it, agents reset like amnesiacs, doomed to repeat mistakes; with it, they evolve into efficient, optimized executors.”
‍
– Gerald Hewes

Memory elevates agents from forgetful responders to intelligent learners. It is typically split into:

Memory Type	Scope	Typical Use
Short-Term Memory	Session-specific	Stored in LLM context and agent session runtime state.
Long-Term Memory	Cross-session	Semantic: factual data (e.g., property tax rates); Episodic: experiences (e.g., past fraud patterns); Procedural: rules (e.g., compliance protocols)

In practice, short‑term memory is often programmatic under the control of the AI. Using LangGraph, it is implemented by updating the shared state of a graph; this state is not persisted beyond the current session.

Example: Remembering a User’s Name in LangGraph (simplified)

from typing import TypedDict, Annotated, List
import operator
from langchain_core.messages import BaseMessage

class AgentState(TypedDict):
    messages: Annotated[List[BaseMessage], operator.add]
    user_name: str | None = None

def greet_and_ask_name(state: AgentState) -> AgentState:
    if state.get("user_name"):
        return {}
    greeting = BaseMessage(content="Hello! What's your name?")
    return {"messages": [greeting]}

def extract_name(state: AgentState) -> AgentState:
    for message in state["messages"]:
        content = message.content.lower()
        if "my name is" in content:
            name = content.split("my name is")[-1].strip()
            return {"user_name": name}
    return {}

def process_with_name(state: AgentState) -> AgentState:
    if state.get("user_name"):
        response = BaseMessage(content=f"Nice to meet you, {state['user_name']}!")
        return {"messages": [response]}
    return {}

# Define workflow
workflow = StateGraph(AgentState)
workflow.add_node("greet", greet_and_ask_name)
workflow.add_node("extract", extract_name)
workflow.add_node("process", process_with_name)

# Add edges
workflow.add_edge(START, "greet")
workflow.add_edge("greet", "extract")
workflow.add_edge("extract", "process")
workflow.add_edge("process", END)

For long‑term storage, typical solutions include relational databases (PostgreSQL, RDS, etc.) for structured preferences, key‑value stores (AWS DynamoDB) for richer properties, graph databases (Neo4j, Dgraph, GraphRAG) for entity relationships, and vector databases (Pinecone, Milvus, Qdrant) for large‑content embeddings.

Frameworks such as Letta (a library that abstracts agent‑memory mechanics) and Mem0 (a universal, self‑improving memory layer for LLM applications) reduce developer effort by handling storage, update, and retrieval while allowing for self‑hosted or cloud‑hosted deployments.

Example: Retrieving Memories with Mem0

def chat_with_companion(user_input: str, user_id: str, companion_id: str):
    # Retrieve relevant memories
    user_memories = search_memories(user_input, user_id)
    companion_memories = search_memories(user_input, companion_id, is_agent=True)

    # Construct message history
    messages = [
        {
            "role": "system",
            "content": (
                "You are a faithful and helpful AI companion with access to both the "
                "user's memories and your own memories from previous interactions. "
                "Your goal is to provide responses that make the user feel good, "
                "supported, and understood. Use the memories to personalize your "
                "interactions, show empathy, and offer encouragement. "
                "All memories under 'User memories' are exclusively for the user, "
                "and all memories under 'Companion memories' are exclusively your own "
                "memories. Do not mix or confuse these two sets of memories. "
                "Use your own memories to maintain consistency in your personality "
                "and previous interactions. Always maintain a positive and uplifting tone "
                "while being honest and respectful."
            ),
        },
        *conversation_history,
        {"role": "user", "content": user_input},
        {
            "role": "system",
            "content": f"User memories: {user_memories}\n\nCompanion memories: 
{companion_memories}",
        },
    ]

    try:
        response = httpx.post(
            "https://openrouter.ai/api/v1/chat/completions",
            headers={"Authorization": f"Bearer {OPENROUTER_API_KEY}"},
            json={"model": "gryphe/mythomax-l2-13b", "messages": messages},
        )
        response.raise_for_status()
        companion_response = response.json()["choices"][0]["message"]["content"]

        # Store new memories
        store_memory([{"role": "user", "content": user_input}])
        store_memory([{"role": "assistant", "content": companion_response}], is_agent=True)

        # Update conversation history
        conversation_history.append({"role": "user", "content": user_input})
        conversation_history.append({"role": "assistant", "content": companion_response})

        return companion_response

    except Exception as e:
        print(f"Error in chat_with_companion: {e}")
        return "I'm sorry, but I'm having trouble responding right now. Please try again."

Mem0 automatically analyses user queries and LLM responses to identify information that should be stored, without explicit programming.

Observability: Taming the Complexity of Agentic Systems

Agentic systems comprise many distributed, interdependent steps. Observability ensures traceability of every execution, leveraging standards such as OpenTelemetry(OTel) for metrics, logs, and traces.

Agentic AI is expensive. A simple chatbot query is one inference call. An agentic task might be a loop of 25 calls: Plan -> Search -> Read -> Think -> Search Again -> Draft -> Critique -> Rewrite. This "Token Tax" can destroy ROI if not managed.

With rich telemetry, key metrics — latency, error rates, request volume, model drift, cost per inference, safety, security, compliance, bias — need to be continuously monitored and alerted on.

Implementing Langfuse Spans for Agent Flows

from langfuse.langchain import CallbackHandler

langfuse_handler = CallbackHandler()

config = {
    "configurable": {
        "property_id": "3442 587242",
        "thread_id": thread_id,
    },
    "metadata": {
        "langfuse_session_id": thread_id,
        "langfuse_user_id": user_id,
        "langfuse_tags": [AGENT_TAG, os.environ["OPENAI_MODEL"]],
    },
}

for question in checklist:
    events = graph.stream(
        {"messages": ("user", question)},
        config,
        stream_mode="values",
    )

    for event in events:
        _print_event(event, _printed)

Just a few lines are all that are needed to forward telemetry data to Langfuse in a LangGraph application.

In large deployments, general‑purpose OTEL platforms such as Uptrace or Jaeger are paired with LLM‑specific tools like Langfuse, LangSmith or Arize AI for detailed agent execution tracing.

Large volumes of trace data require dedicated observability platforms. AI systems can monitor and alert in real time — a capability often overlooked during planning. Best practice dictates (and our engineering teams have found) integrating monitoring with CI/CD pipelines so that the evolution of agentic systems is understood as changes are deployed.

Building the Future, One Agent at a Time

The leap from reactive chatbots to proactive agentic systems is not just a technical upgrade — it’s a strategic shift. Enterprises that win will be those that invest in robust memory, orchestration, observability, and responsible-by-design AI. At Proxet, we don’t treat responsible AI as an afterthought — we build and deploy our own AI models and frameworks to address security, safety, reliability, and ethics from day one. These practices are backed by concrete guidelines, best practices, and continuous evaluation strategies that ensure agentic systems behave as intended as they scale.

Coming Up Next

In our next post, we’ll explore the Data Layer — how to architect federated data systems that feed your agentic stack. We’ll cover schema design, security, and performance considerations that keep data flowing smoothly to your autonomous agents.

How Proxet Can Help

Proxet partners with forward-thinking enterprises to design and build complex adaptive systems where autonomy and accountability go hand in hand. If you’re ready to move beyond the Chatbot paradigm and unlock the value of true Autonomy, let’s start a conversation.