Chatbot, AI assistant, and agentic workflow
A chatbot primarily responds to prompts in a conversational interface. It may answer questions, provide guidance, or retrieve information. An AI assistant typically supports a person with knowledge work such as drafting, summarizing, searching, or analysis; the person remains responsible for carrying out the operational steps.
An agentic workflow can take a goal, plan a sequence of steps, consult approved information, call authorized tools or APIs, verify results, and continue or escalate. This does not mean unrestricted autonomy. The workflow should operate within explicit permissions, policies, and checkpoints.
Where agentic AI may create value
Potential candidates include preparing case summaries from approved records, routing service requests, assembling information for internal reviews, and coordinating routine steps across connected business applications. These examples should be treated as discovery opportunities, not assumptions that every organization needs an AI agent.
Agentic AI is less suitable when the process is poorly defined, data is unreliable, errors are difficult to reverse, or decisions require accountable human judgment. A standard application, API integration, or rules-based automation may be simpler and more dependable.
How to evaluate a use case
- Business value: what delay, cost, error, or service bottleneck should improve?
- Process clarity: are inputs, steps, exceptions, and outputs understood?
- Data readiness: can the system access reliable information with appropriate permissions?
- Action boundaries: which tools may it use, and which actions require approval?
- Verifiability: can the organization independently check task quality and completion?
- Ownership: who monitors performance and handles exceptions?
Measure outcomes and manage risk
Establish a baseline before a pilot. Relevant measures may include cycle time, handling time, manual handoffs, completion rate, rework, error rate, escalation rate, and user satisfaction. Include the full operating cost: model usage, infrastructure, integration, monitoring, human review, security controls, and maintenance.
Give agents only the permissions required for their tasks. Use human approval for high-impact actions, log important activity, test failure scenarios, and define a safe way to stop or escalate. NIST’s Generative AI Profile and OWASP’s LLM guidance provide useful references for risk management and application security.
A sensible first step is one bounded workflow with agreed success criteria. Souldigi’s proposed approach is to assess the process, data, integrations, and risks before defining a pilot. The objective is to prove practical value—not to automate everything simply because AI is available.
Sources and further reading
NIST AI 600-1, Generative AI Profile: https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
OWASP LLM Top 10: https://genai.owasp.org/llm-top-10/
