Agentic AI plans and executes multi-step tasks autonomously, calling systems to resolve issues end-to-end without human input at each step.
Agentic AI refers to AI systems that plan, decide, and execute multi-step tasks without constant human instruction. Unlike chatbots that respond to a single question and stop, agentic AI takes sequences of actions across systems - reading data, making changes, resolving issues end-to-end. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention - reducing operational costs by 30% in the process.
Agentic AI completes multi-step tasks - processing refunds, updating accounts, escalating issues - without waiting for human confirmation at each step.
Unlike scripted bots, agentic AI adapts mid-workflow when it hits exceptions, making it viable for complex and variable ticket types.
Effective deployment requires defined authorization scopes: what the AI can act on alone, what needs approval, and what humans must own entirely.
Human-in-the-loop configurations let AI handle routine steps autonomously while routing high-risk decisions to human agents for a final call.
Customer service teams face a specific problem: AI that can answer "what's my balance?" falls apart on "I was charged twice, my account is locked, and I need a refund before tomorrow." These multi-step, multi-system issues represent the hardest 20% of ticket volume - and the 20% that drives the most churn when mishandled. Agentic AI is built for exactly this. It does not just answer. It acts.
Most automation tools help agents work faster. Agentic AI removes the agent from the loop entirely - for the ticket types where that is safe to do. The distinction matters more than it sounds.
What Is Agentic AI?
Agentic AI is an AI system that plans and executes multi-step tasks autonomously. Where a traditional model responds to a question and stops, an agentic system breaks a goal into steps, calls the tools or systems it needs, acts on each result, and continues until the task is done or it reaches a decision it is not authorized to make.
The term distinguishes it from reactive AI - systems that respond to one input and wait. Agentic AI loops: read output, decide next step, call next tool, continue. This makes it fundamentally different from FAQ bots and decision-tree chatbots. Those systems deflect. Agentic AI resolves. The core components are a large language model that plans and reasons, APIs or tools the AI can call, a memory layer tracking what has been done, and a guardrail layer defining what it can do without human sign-off.
How Does Agentic AI Work?
Agentic AI works by breaking a customer request into a sequence of steps, executing each one using connected tools, and adapting based on what it finds. Rather than responding once and waiting, it runs a loop - act, observe, decide - until the goal is reached or a human handoff is triggered.
Planning and reasoning
When a customer submits a request, the AI builds a plan rather than retrieving a static answer. "The customer wants a refund on order 4821. Verify the order, check policy, confirm return window, initiate refund, confirm to customer." Each step depends on the result of the last. If the order is ineligible, the AI adjusts - it does not force a predefined path when the facts do not fit it.
Tool use and system access
To execute those steps, the AI calls tools: APIs connected to your CRM, order management system, payments platform, or knowledge base. When it queries the CRM and finds an open dispute flag, it adapts. This is the core difference from scripted automation: agentic AI handles exceptions because it can reason about them. What it can resolve is directly tied to the systems it can access and act on.
What Can Agentic AI Actually Handle?
Agentic AI can handle any customer issue that follows a discoverable logic - where the right action depends on data readable from connected systems. In customer service, this covers most of the high-volume work that currently requires an agent to investigate, decide, and act.
Refunds and cancellations. The AI verifies eligibility, initiates the refund in the payments system, and confirms to the customer without human involvement. When AI handles the full resolution workflow, the customer interaction time drops from minutes of back-and-forth to a single automated exchange - Klarna reported cutting overall resolution time from 11 minutes to under 2 minutes across their AI-handled tickets in 2024.
Account changes and permissions. Plan upgrades, address updates, user permission adjustments, and billing modifications are executable once policy guardrails are satisfied - no agent needed for the lookup-and-act portion of the work.
Multi-system issue resolution. When a problem spans your CRM, billing platform, and product backend - a common pattern for subscription businesses - agentic AI queries all three, reconciles the data, and takes the right action.
Ticket enrichment and routing. For issues requiring human judgment, agentic AI does the investigative work first: pulling account history, summarizing context, routing a briefed ticket rather than a raw complaint.
Proactive outreach. Agentic AI detects issues before customers report them - a failed payment, a delayed shipment - and reaches out with a resolution rather than a generic alert.
What Results Can You Expect?
Results depend on two variables: how many ticket types follow a discoverable logic, and how many backend systems the AI can access with read-write authority. Leading deployments from vendors like Salesforce Agentforce and Intercom Fin are already resolving 60-84% of queries without human escalation in select implementations. Typical deployments today see resolution rates of 40-65%, depending on implementation maturity and use case scope.
First-response time drops from hours to seconds - the AI starts working the moment a ticket arrives. For fully automated tickets, agent handle time approaches zero. By contrast, SQM Group's 2024 contact center benchmarking data puts average agent handle time at 11-12 minutes including wrap-up - giving a clear sense of the efficiency gap when AI takes the workflow entirely. Customer satisfaction scores for AI-resolved tickets match or exceed human scores when resolutions are complete, not deflections dressed as answers. The human team shifts to the 20% of cases that need judgment, and that shift is where CSAT improvement shows up on scorecards.
What Are the Limits of Agentic AI?
Agentic AI does not replace human judgment - it removes humans from tasks that follow a discoverable logic. Issues involving emotional nuance, regulatory complexity, disputed liability, or situations where policy does not cover the outcome still need a human in the decision loop.
The clearest guardrail is irreversibility. Agentic AI should be authorized to take reversible actions - refunds that can be re-charged, account changes that can be rolled back. High-risk or legally consequential actions should require human approval. Compliance-related issues - GDPR data requests, insurance claims, regulated financial transactions - are cases where AI can research and prepare a recommendation but should not finalize. A practical framework: define "AI acts," "AI recommends and human approves," and "human owns." Most ticket volume fits the first two categories. For more on drawing these lines, see our guide to what should never be automated in customer service.
Key Takeaways
Agentic AI executes multi-step workflows end-to-end - leading deployments resolve 60-84% of queries without human escalation, with typical deployments achieving 40-65% as implementation matures.
The difference from chatbots is action: agentic AI plans, queries systems, and resolves; chatbots respond to questions and wait for humans to act.
Guardrails matter more than capability: define what AI can decide alone, what requires approval, and what humans must own before you deploy.
CSAT for AI-resolved tickets matches human scores when resolutions are genuinely complete - not deflections packaged as answers.
Agentic AI is not a more sophisticated chatbot. It is a different approach to automation - one that asks "can this issue be resolved?" rather than "can this question be answered?" For customer service teams, that distinction determines whether AI reduces cost or actually improves customer outcomes.
The teams seeing the most impact are not those with the most advanced models. They are the ones that gave their AI real system access, defined clear authorization boundaries, and measured success in resolutions - not deflections.
If you are evaluating agentic AI for a complex, regulated, or multi-system support operation, Lorikeet is built for exactly this. See how teams use it to handle the tickets that standard automation has historically failed to resolve.
