Introduction
AI agents are transforming the way businesses operate. Unlike traditional chatbots, agents can independently solve complex tasks, use tools, and execute multi-step workflows.
In this article, we outline the path from evaluation to production deployment.
What Are AI Agents?
An AI agent is a system that:
- Understands goals and independently plans sub-steps
- Uses tools such as APIs, databases, and code execution
- Works iteratively, reviewing results and adjusting the approach
- Maintains context across multiple interactions
Distinction from Chatbots
While a chatbot responds to individual questions, an agent can work through a complete workflow: from analysis through implementation to validation. When agents need access to internal company knowledge, RAG systems for the enterprise are the natural next step. For connecting agents to existing tools like Jira or SAP, Model Context Protocol (MCP) is the relevant standard.
Evaluation: Finding the Right Use Cases
Not every task is suited for AI agents. Good candidates include:
- Repetitive, rule-based processes with clearly defined inputs and outputs
- Research-intensive tasks where information from multiple sources needs to be consolidated
- Code reviews and documentation where consistency and completeness matter
Concrete examples from daily operations are covered in AI Automation in Daily Work: 5 Use Cases. Teams that take governance and risks of autonomous agents seriously should also read our piece on OpenClaw and enterprise risks.
Implementation
Implementation typically occurs in three phases:
Phase 1: Proof of Concept
- Define use case and success criteria
- Set up a prototype with an LLM provider
- Run initial tests with real data
Phase 2: Pilot
- Integration with existing systems
- Feedback loops with business units
- Build monitoring and logging
Phase 3: Rollout
- Scale to additional teams and use cases
- Establish governance framework
- Continuous improvement process
Conclusion
AI agents aren’t hype. They’re a practical tool that delivers real value when introduced correctly. The key lies in systematic evaluation and an iterative rollout.
We help businesses successfully deploy AI agents. Let’s talk →
Frequently Asked Questions
What is the difference between a chatbot and an AI agent?
A chatbot responds to individual questions in a conversation. An agent understands complex goals, plans sub-steps, uses tools, and works iteratively through an entire workflow. Agents can access databases, run code, call APIs, and adjust their approach based on results. Chatbots are reactive, agents are proactive multi-step problem solvers.
Which use cases are best for AI agents?
Best candidates are repetitive rule-based processes with clear inputs and outputs, research-intensive tasks consolidating information from multiple sources, and code review or documentation tasks requiring consistency. Support ticket triage, data quality checks, and infrastructure audit automation are practical examples. Avoid open-ended creative tasks where evaluation criteria are subjective.
How long does proof of concept take?
A solid proof of concept takes two to four weeks. Define the use case and success criteria, build a prototype with an LLM provider, and test against real data. Success at this stage means the agent handles 80 to 90 percent of cases correctly. This reveals whether the concept is viable before investing in integration and production deployment.
What is RAG for AI agents?
RAG (Retrieval-Augmented Generation) augments agents with access to internal company knowledge. Instead of relying only on training data, agents retrieve relevant documents from company databases when answering questions. This enables agents to work with up-to-date information, proprietary data, and company-specific context without retraining the underlying model.
What governance should I have for AI agents?
Establish clear approval workflows for high-risk actions. Agents should flag decisions requiring human review before execution. Implement detailed logging of all agent actions and reasoning. Define escalation paths when agents encounter unfamiliar situations. Regular audits and feedback loops ensure agents stay accurate. For regulated industries, document all decisions and maintain human accountability for outcomes.
What are enterprise AI agents?
Enterprise AI agents are autonomous software systems that use large language models to plan and execute multi-step business workflows without constant human instruction. Unlike consumer-grade chatbots, enterprise AI agents integrate with internal tools (CRM, ERP, ticketing systems), access company data securely, operate within defined governance guardrails, and produce auditable outputs. They are deployed to automate knowledge work at scale — from research and reporting to support triage and process orchestration.
