- AI Agent
- AI Integration
- System Architecture
- B2B
Most companies talk about AI Agents while running a chatbot. The difference is not the language model. It is the ability to plan and act on real systems. This whitepaper breaks down the architecture of an enterprise grade AI Agent and a straight roadmap to deploy one.
What an AI Agent is, a technical definition
An AI Agent takes a goal, decomposes it into steps, calls tools and APIs to execute, then observes the result and adjusts until the goal is met. A chatbot stops at a pattern based answer; an agent closes the loop between reasoning and action.
The four core components of an enterprise AI Agent
Profiling: defining the role
Profiling sets who the agent is, what it may do and where it is blocked. A support agent differs from a warehouse operations agent in its tool scope, tone and autonomy threshold. Clear profiling is the first control layer, tied to role based permissions.
Memory: short and long term
Short term memory holds the current session context. Long term memory stores enterprise knowledge as embeddings in a vector database such as Qdrant or Milvus, retrieved semantically through RAG. This lets the agent answer from your real documents rather than generic training knowledge.
Planning: decomposing the task
This is what separates an agent from a chatbot. The agent decomposes a goal into a sequence of steps using techniques like Chain-of-Thought and, in particular, the ReAct loop: reason, act, observe, then repeat. On each turn the agent decides the next tool based on the result it just received.
Action: acting on real systems
The agent calls internal APIs, queries databases, updates records and triggers workflows. This is where real value is created: the agent does not just answer, it executes, under the control of the profiling layer and full audit logging.
The OKAXI enterprise deployment architecture
A private LLM, isolated agent
The biggest barrier to running an agent is data leakage. OKAXI deploys agents on a Private LLM installed on-premise or in a private cloud, so sensitive data never leaves your infrastructure. Every access runs through role based permissions, full audit logging and NDA compliance with partners, a hard requirement for international clients.
Microservices and Kafka at scale
An agent calls many tools and processes many requests at once. OKAXI splits each capability into an independent service in Python and C#, communicating asynchronously through the Apache Kafka message broker. When a service is overloaded or under maintenance, messages wait in the queue and reprocess once ready, so the system has no bottleneck. This is the foundation of an AI integration for business that holds real load.
Market data
Gartner forecasts that by 2028 about 33% of enterprise software will include agentic AI, up from less than 1% in 2024, and that at least 15% of day to day work decisions will be made autonomously by agents. McKinsey estimates generative AI could add 2.6 to 4.4 trillion US dollars in value per year across use cases, much of it from operational automation. The signal is clear: agents are moving from experiment to infrastructure.
A practical roadmap
Do not start with a do everything agent. Pick one repetitive, measurable process with clear boundaries. Pilot it on a Private LLM, measure the self serve rate and accuracy, then extend to the next process. A phased approach lowers risk and shows ROI early.
OKAXI starts with a workshop to pick the right agent use case for your operations, then designs an enterprise AI Agent architecture wired into your existing data infrastructure, rather than spreading automation thin.