AI agents are a fixture in today’s technology discourse. But what exactly makes a system an AI agent? The answer is increasingly unclear. As the hype accelerates, vendors often stretch the definition, repackaging traditional assistants and workflows under the agentic label. This "agent washing" has sparked confusion and skepticism, and it threatens to obscure the transformative potential of truly autonomous systems.
Agent washing: Where existing assistants are rebranded as agents without the underlying intelligence or integration needed. (Gartner)
To cut through the noise, we need to revisit the core characteristics of AI agents, understand where today’s implementations fall short, and explore how connected data infrastructure can activate real agentic capabilities.
Defining an AI agent
Salesforce’s Agentforce initiative defines AI agents as “autonomous software programs that pursue goals by perceiving their environment, reasoning over information, and taking actions.” Forrester and Salesforce both describe agents in similar terms: not as scripted tools, but as systems capable of goal-driven, adaptive behavior.
For the purposes of this discussion, we define an AI agent as a system (autonomous or assisted) that:
Independently interprets and responds to its environment using data
Makes context-aware decisions aligned with specific objectives
Initiates and manages actions across digital systems
Learns or adapts from experience, feedback, or evolving context
This vision is compelling. In theory, AI agents could handle procurement workflows, schedule fleet maintenance, manage security responses, or even execute multistep financial reporting.
Agents: are they autonomous or not?
AI agents do not need to be fully autonomous to qualify as agents. Autonomy exists on a spectrum, and many agents in production today involve some level of human involvement.
At a foundational level, an agent is any system that can perceive its environment, reason over information, and take goal-directed action. It may require human approval to act (human-in-the-loop) or operate independently with optional human oversight (human-on-the-loop). Fully autonomous agents represent the high end of this spectrum — systems that operate without intervention, adapt to changing inputs, and trigger workflows on their own.
Most popular frameworks include both assisted and autonomous agents. What matters most is transparency: specifying how autonomous the system is helps set realistic expectations, especially for technical buyers or AI-native audiences.
Retail examples: What is and isn't an AI agent
To bring this definition into focus, quiz yourself on these examples from the retail industry. Decide whether each scenario meets our definition of agent or not and then click the scenario to see if we agree:
A dynamic pricing agent that continuously monitors competitor pricing, stock levels, and customer demand to adjust product pricing in real time
Yes. It perceives changing conditions, makes context-aware decisions, and takes autonomous action.
A product recommendation engine that surfaces items based on simple collaborative filtering, without adapting to recent behavior or context
No. It uses a fixed algorithm without dynamic reasoning or goal-oriented adaptation.
A customer experience agent that monitors real-time behavioral signals, identifies churn risk, and launches coordinated retention campaigns across email, SMS, and loyalty platforms
Yes. It integrates perception, decisioning, and action across multiple systems to achieve a goal.
A chatbot that answers store hours and return policy questions using predefined scripts
No. It follows static, rule-based logic and lacks perception, reasoning, or action.
A workflow automation tool that notifies staff of low inventory but cannot take further action or reprioritize tasks
No. It performs a single scripted task and lacks decision-making or independent action.
An inventory optimization agent that forecasts demand, detects anomalies, and automatically initiates transfers or reorders across distribution centers
Yes. It reasons over real-time data and executes tasks aligned with operational goals.
Most "agents" aren’t there yet
In practice, most agent implementations fall short of this definition. According to Forrester’s July 2025 report, the industry is still grappling with how to define and measure agent maturity. Many deployments rely heavily on scripted rules and lack the decisioning or action layers necessary for autonomy.
Gartner echoes this concern. By 2027, they expect over 40% of agentic AI projects will be canceled due to inflated claims and underwhelming outcomes.
A maturity framework for enterprise AI agents
Forrester outlines their maturity model based on three capabilities:
Analytics: Can the agent perceive and interpret its environment?
Decisioning: Can it weigh options and make informed choices?
Action: Can it execute tasks across systems and services?
Most enterprise AI agents are stuck at stage one. They surface insights or summarize content but lack agency. Moving up the maturity curve requires deep integration with operational data.
Data access is the key to agent maturity
Underdeveloped agents are often siloed from enterprise data, limiting their capabilities to basic analysis or isolated workflows. To evolve beyond simple capabilities, agents need access to the same data humans rely on to make decisions. That includes customer records, financial systems, product telemetry, and more. But accessing and integrating this data is a major challenge.
Data lives across cloud apps, on-premises systems, APIs, and databases. It comes in varied formats, structures, and latencies. Without seamless, secure connectivity, even the most capable AI model is functionally blind.
That’s where CData comes in. CData’s Model Context Protocol (MCP) Servers provide real-time, governed access to enterprise data across hundreds of sources. Through standard protocols and interfaces, agents can query data directly from Salesforce, NetSuite, SAP, Snowflake, and more, without brittle ETL pipelines.
Through our MCP servers, AI agents can:
Dynamically pull context for better decisioning
Validate outputs against authoritative systems
Trigger downstream actions across applications
Whether orchestrating fully autonomous agents or integrating decisioning into customer-facing services, real-time data access through MCP Servers is essential to agentic maturity.
Transform decision-making with clarity, not hype
As enterprises evaluate AI agent investments, precision matters. Not every chatbot or workflow qualifies as an agent. Without autonomy, data awareness, and integrated action, the term loses meaning.
But the potential remains. With the right foundation—seamless access to trusted data—AI agents can transform decision-making, reduce operational friction, and generate new value across the enterprise.
To explore how CData can power your next generation of AI agents, visit our MCP Server page where you can see the servers in action and download the free beta!
Try CData MCP Servers Beta
As AI moves toward more contextual intelligence, CData MCP Servers can bridge the gap between your AI and business data.
