Conceptual Overview

How We Think About AI Agents

At Dataworkz, an Agent is not a chatbot, a prompt, or a single LLM call. An Agent is a goal-oriented software system that reasons, plans, and acts across multiple steps to solve real business problems.

We view Agents as the natural evolution of RAG: moving from answering questions to getting work done. Agents as Reasoning Systems, Not Just Responses

A Dataworkz Agent is built around three core capabilities:

Reasoning and Decision Making

Agents use LLMs as reasoning engines. Instead of returning a single response, the Agent decides what needs to be done next, evaluates intermediate results, and adapts its plan dynamically. Decision making may involve conditional logic, iteration, user clarification, or graceful failure handling.

Multi-Step Planning and Execution

Agents break down a user’s request into a sequence of executable steps. Each step invokes a tool with the right parameters, often derived from:

• Conversation history

• Execution context

• Environment state

• Results of previous steps

Planning is iterative. An Agent may partially solve a problem, reassess, and continue planning until it can confidently finish or determine it cannot proceed further.

Working Memory and Context Awareness

Agents maintain working memory across steps. This allows them to resolve references, reuse intermediate results, and preserve context across complex interactions. For example, an Agent can infer that “the order with the socks” refers to a specific order discussed earlier without re-asking the user.

Tools Are the Agent’s Skills

In Dataworkz, tools define what an Agent can do.

A tool represents a discrete capability such as:

• Querying structured data (databases, warehouses)

• Calling external APIs

• Retrieving knowledge from RAG applications

• Performing transformations or validations

Each tool is strongly described using: •

• A clear name and description

• Explicit input parameters with types

• Structured outputs with semantic meaning

This structure allows Agents to reason about tools, chain them together, and reliably pass outputs from one step to the next.

Tools are reusable, account-scoped, and managed centrally through the Tools Repository, enabling consistency and governance across Agents.

Typed Data Is a First-Class Concept

Agents operate across both structured and unstructured data. To make this reliable, Dataworkz treats types as foundational.

Types define:

• The shape of data

• Valid values and formats

• How outputs from one tool can safely feed into another

By enforcing typed inputs and outputs, Agents can:

• Chain tools predictably •

• Reduce hallucinations

• Improve reasoning accuracy

• Support enterprise-grade reliability

Scenarios Provide Focus and Guardrails

Rather than exposing every tool to every question, Dataworkz introduces Scenarios.

A Scenario:

• Represents a logical cluster of related use cases

• Defines a bounded toolset relevant to those use cases

• Acts as both a reasoning aid and a safety boundary

When a user asks a question, the Agent first determines which Scenario applies. This reduces ambiguity, improves tool selection, and prevents Agents from operating outside their intended scope.

Execution Is Enterprise-Grade by Design

Behind every Agent is a Tool Execution Framework that handles:

• Invocation of heterogeneous systems

• Error handling and retries

• Observability, logging, and auditability

• Feedback loops to the reasoning engine

This separation of reasoning from execution allows Agents to remain flexible while still meeting enterprise requirements for security, reliability, and governance.

Agents Are Declarative, Inspectable, and Composable

A Dataworkz Agent is defined declaratively using configuration rather than hard-coded logic. This makes Agents:

• Easier to understand and audit

• Easier to evolve over time

• Safer to deploy in regulated environments

Agents are composed of Scenarios, Tools, and policies rather than opaque prompt chains.

How It All Comes Together

When a user interacts with a Dataworkz Agent:

• The Agent interprets the request and selects the appropriate Scenario.

• The reasoning engine plans a sequence of steps.

• Tools within the Scenario are invoked in the correct order.

• Results are stored in working memory and reused across steps.

• The Agent decides whether it can complete the task, needs to iterate, or must ask for clarification.

This architecture allows Dataworkz Agents to move beyond static Q&A and into context-aware, multi-step problem solving across structured data, unstructured knowledge, and external systems.

From Answers to Outcomes

In summary, Dataworkz Agents are:

• Goal-driven, not prompt-driven

• Reasoning systems, with deterministic workflows

• Tool-orchestrators, not isolated models

• Enterprise-ready, not experimental prototypes

They are designed to move organizations from retrieving information to executing decisions with confidence and control.