Agentic AI and the Rise of Digital Employees
Agentic Artificial Intelligence (AI) represents a shift from models that simply generate responses to systems that can plan tasks based on defined goals, execute multi-step processes, interact with tools, and iteratively improve outcomes.
Onur Koç
Venture Partner, Boğaziçi Ventures
Author: Artificial Intelligence for a Better World / Secrets of Artificial Intelligence for Children
3rd Book: 5th Dawn, to be released in March on Amazon (including Amazon UK) and all major digital platforms.
Agentic Artificial Intelligence (AI) represents a shift from models that simply generate responses to systems that can plan tasks based on defined goals, execute multi-step processes, interact with tools, and iteratively improve outcomes. In other words, it introduces the concept of Digital Employees.
Today, Agentic AI is one of the most discussed topics in technology, and many companies are actively exploring its potential.
According to McKinsey’s 2025 global survey, 23% of respondents reported that their organizations have already experimented with at least one Agentic AI system, while 39% said they are actively exploring opportunities with agentic systems.
Intelligent AI agents—often referred to as Digital Employees—can act in real-world environments, plan tasks, and take multiple actions within a workflow.
However, while agentic systems bring greater capabilities, they also introduce a larger risk surface. Gartner predicts that more than 40% of agentic AI projects may fail by the end of 2027, due to factors such as uncertain business value, cost concerns, and risk management challenges.
Despite these risks, we are witnessing the rapid emergence of startups focused on this space.
With Agentic AI, the future is not just software tools—but digital teammates working alongside organizations to produce real outcomes.
Core Characteristics of Digital Employees
Most traditional (non-agentic) AI applications operate in a simple pattern:
prompt → response
or
prompt → RAG → response
The Digital Employee / Agentic approach transforms the model into an autonomous or semi-autonomous process executor that has access to tools and can carry out tasks.
Typical core characteristics of Agentic Digital Employee systems include:
Goal and Task Planning
Instead of simple task automation, the system starts with a high-level objective, breaks it into subtasks, and executes a plan. The ability to revise the plan dynamically is critical.
Tool Usage and Orchestration
Agents can interact with tools such as search systems, data retrieval tools, computational services, and enterprise APIs, enabling them to manage sequential or conditional workflows.
State and Memory
They maintain session context as well as long-term organizational memory, allowing them to improve performance over time.
Iterative Feedback Loops
Modern agent frameworks explicitly define feedback loops:
tool output → validation → retry if necessary.
Constrained Autonomy
Digital Employees operate within defined permissions, policies, and governance boundaries, often requiring human approval for critical actions. Without such guardrails, autonomy could significantly increase security risks.
Comparing Traditional AI vs. Agentic AI / Digital Employees:
Technical Tools and Development Platforms
Agentic AI / Digital Employee solutions are not a single product category. Instead, they typically combine:
Agent frameworks
Cloud agent services
Integration standards
Observability and monitoring tools
Open-source agent frameworks enable rapid prototyping, while cloud agent services provide the operational layer required for production environments, including scalability, governance, security, and monitoring.
Over the past two years, new standards have significantly accelerated the agentic ecosystem.
One of the most important is MCP (Model Context Protocol), an open standard that enables agents to securely connect with external tools and data sources through two-way communication.
You can think of MCP as the HTTP of Digital Employees and Agentic AI systems.
Below are six of the most popular tools used to build Digital Employees.
Digital Employee Use Cases
Today, several companies are rapidly developing solutions that transform how work gets done. Five of the most common AI-powered Digital Employee use cases include:
HR Onboarding Digital Employee
Automates onboarding processes for new hires by planning training, managing documentation, and provisioning system access.
Marketing Digital Employee
Analyzes campaigns, generates audience-specific content, and continuously optimizes marketing activities.
IT Support Agent
Analyzes user issues, resolves technical problems, and can take automated actions within systems to accelerate support processes.
Legal Agent
Reviews contracts and legal documents, identifies risks, and accelerates the preparation of standardized legal documentation.
Customer Experience Digital Employee
Understands customer requests, generates solutions, tracks processes, and improves end-to-end customer experience.
Impact on Entrepreneurship and Business Models
Traditional SaaS solutions provide tools.
The Digital Employee approach delivers outcomes.
This shift creates five major opportunities for startups.
Vertical Agents: Work-as-a-Product
Competition in the Agentic AI era will not be about generic chat interfaces, but about agents that deliver specific business outcomes such as:
Insurance claims processing
E-commerce returns
KYC verification
Payroll management
IT incident resolution
The concept of an “Agentic Organization” also points to faster decision cycles in areas such as budgeting and reporting.
Agent Infrastructure: Observability and Security
Agentic AI startups must sell more than just an agent demo.
They must provide clear ROI, cost transparency, and risk control mechanisms.
For example, open-source autonomous agent platforms such as OpenClaw can interact with computer systems via graphical interfaces and APIs, much like a human user. These agents can:
Click buttons
Fill out forms
Navigate between applications
Complete end-to-end tasks
However, reliability and security are critical. Preventing unauthorized access to corporate data, protecting sensitive information, and ensuring malicious actors cannot gain control of devices through such platforms must be top priorities.
As autonomy increases, strict permission boundaries and real-time monitoring become essential.
Integration Standards and Ecosystem Economy
Standards like MCP reduce integration costs and enable access to a wide range of tools and data sources.
This opens two strategic opportunities for startups:
MCP-compatible tools and connector marketplaces
Security and compliance layers built on top of MCP
Risks: Agent Washing, Cost, and Trust
There are also several key risks in the agentic AI space.
Agent Washing
Solutions that are not truly agentic may be marketed as such, eroding trust among investors and customers.
Cost
Multi-step workflows and multiple tool calls can significantly increase token and compute costs. Companies must measure unit economics carefully, such as cost-per-case or cost-per-resolution.
Security
Threats like prompt injection and excessive autonomy require robust authorization design and auditing mechanisms, particularly in enterprise environments.
In the era of Agentic AI, the “product” is no longer simply a software subscription. It is a Digital Employee that reliably produces business outcomes. The startups that will win in this new paradigm will not sell models—they will sell processes: measure them, control them, and continuously improve them.