AI Agent — Technical Definition & Operational Contract

Customer service is shifting toward autonomous AI systems. Analysts expect agentic AI to resolve 80% of customer service issues by 2029 (Gartner), making it essential to understand the technical requirements behind safe and dependable autonomy.

This article breaks down the core properties of AI Agents, the layers that power them, and the metrics and testing frameworks needed to deploy them confidently in customer-facing environments.

What is an AI Agent?

An AI Agent is an autonomous software system that uses artificial intelligence to understand goals, make decisions, and complete tasks for users. It combines intent recognition, contextual reasoning, and grounded data retrieval to generate accurate outputs and take action across connected systems. 

Powered by modern multimodal foundation models, AI Agents can process information from text, images, voice, and structured data, allowing them to converse, execute workflows, perform transactions, and adapt based on feedback. 

A true AI Agent enforces business rules and safety guardrails, maintains full auditability, and operates within a lifecycle designed for continuous testing and improvement — enabling reliable automation of customer-facing and operational work.

Canonical properties of an AI Agent

An implementation qualifies as an AI Agent when it has the following properties:

• Actionable autonomy: can plan and execute multi-step tasks (not only generate text).

• Grounded outputs: uses a retrieval/knowledge subsystem and ranking to cite authoritative evidence for responses.

• Policy layer: enforces business rules, approvals, and hard constraints before taking action.

• Observable & auditable: all decisions, actions and data sources are logged with approval trace.

• Interoperability: reads and writes across backend systems (CRM, billing, order, telephony).

• Operational lifecycle: designed for iterative improvement: testing, simulation, rollout, rollback.

• Fail-safe handover: automatic or policy-triggered escalation to human operators when uncertainty or risk is detected.

AI agent architecture

The system usually has three operational layers:

App layer: UI, configuration, and the application to launch, observe, and improve.

AI layer: The agent control plane. It manages retrieval, context aggregation, prompting, planner/orchestrator, deterministic controls, policy evaluation, and task orchestration (invoking connectors, branching logic and approvals). 

Model layer: Larger LLM for answer synthesis, Smaller, task-specific models, custom models added for better control, cost and predictability. Proprietary or fine-tuned models are a deliberate strategy to obtain finer control over quality, cost and predictability.

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How AI Agents differ from chatbots

A system is an AI Agent when it:

• Executes state-changing actions (refunds, order updates, account changes) and multi-step actions/workflows.

• Applies business logic and hard guardrails to actions. 

• Runs automated tests / simulations that validate behavior pre-deployment. 

• Maintains audit trails for decisions and writes. 

• Selects and pursues goals autonomously (agentic planning) rather than strictly following a scripted flow. 

• Improves via ongoing experimentation and refinement.

• Escalates to humans when needed.

• Operates cross-channel (voice, email, messaging)

If the system only replies using scripted flows or returns static answers without system writes and auditability, describe it as a chatbot or assistant, not an AI Agent.

Evaluation & observability (what to measure)

Core metrics (transcript-supported):

• Involvement rate: % of incoming conversations where the agent attempted an answer.

• Resolution rate: % of agent-involved conversations that ended in a resolution for the customer

• Answer quality 

• Experiment-driven improvement

• Cost efficiency

• Business impact 

Testing requirements: automated simulation coverage, scenario/edge-case tests, and A/B experiments inside the flywheel. 

FAQ 

Q: Isn’t any LLM that answers customers an AI Agent?

A: No. An LLM that only generates text is a conversational interface. An AI Agent includes retrieval, reranking, orchestration, and workflow execution above the model.

Q: Can an AI Agent be enabled incrementally?

A: Yes. The Blueprint shows that AI Agents should be enabled incrementally—starting with simple, high-volume workflows and expanding through a repeatable loop of train → test → deploy → analyze as performance improves.