An AI Agent That Resolves Tickets, Not Just Answers Them
An autonomous agent takes a support ticket, works across your helpdesk, CRM, and payment systems, pauses for human sign-off on the risky step, and closes the loop.
A chatbot replies. An agent acts. This example shows a support-resolution agent handling a real workflow end to end: it reads the ticket, verifies the problem against your systems, applies your policy, escalates the high-value action to a person, then issues the refund and notifies the customer. The value of an agent is invisible in a screenshot, so watch it work below.
Watch the Agent Work
A goal comes in. The agent plans, calls four systems, hits a guardrail and waits for a human on the refund, then finishes with a real outcome.
Support resolution agent
Autonomous: plans, calls tools, takes action
Incoming ticket #4821
"I was charged twice for order #BN-4821. Please fix this."
Illustrative example. Names, numbers, and systems are fictional.
Before / after
Manual triage and refund: roughly 6 minutes per ticket.
With the agent: about 14 seconds, with human sign-off on refunds over $100.
What the Agent Does
Plans toward a goal
It turns a ticket into a plan, decides which steps and tools are needed, and adapts as it learns more, instead of following one rigid script.
Calls your real tools
It reads and writes across your helpdesk, CRM, and payment systems through their APIs, so it can actually take action, not just describe one.
Stops at guardrails
Spend limits, allowed actions, and policy rules are enforced in code. The agent cannot exceed what you authorize.
Keeps a human in the loop
High-value or sensitive actions pause for one-click approval. Routine cases finish on their own.
Logs every action
Every tool call, decision, and approval is recorded, so you can see exactly what the agent did and why.
Closes the loop
It writes the outcome back into your systems and notifies the customer, leaving a clean, auditable trail.
How We Build It
Scope the workflow
We map one high-value workflow end to end: the goal, the decisions, the systems it touches, and exactly where a human must sign off.
Build the integration layer
We connect the agent to your tools through their APIs with scoped credentials, so it reads and writes only what it should.
Guardrails and evals
We encode your policies as hard limits and test the agent against real and edge-case scenarios until its behavior is reliable, not just impressive in a demo.
Human-in-the-loop
We add approval checkpoints for the actions you choose, with the full context attached so a person can decide in seconds.
Deploy and monitor
We ship it with full observability, alerting, retries, and cost controls, then tune it based on real runs.
Deliverables
- Custom autonomous agent for one workflow
- Tool/API integrations (helpdesk, CRM, payments, email)
- Policy guardrails and spend limits
- Human-in-the-loop approval interface
- Observability dashboard and audit logs
- Evaluation suite and deployment
Technologies
Frequently Asked Questions
How is an agent different from a chatbot?
Will it act without our approval?
Can we see what the agent did?
Is it safe for regulated or PDPL environments?
What does an agent project cost and how long does it take?
Part of our Agentic AI service
Agentic AI Development
This is one example of an agent we build. We design autonomous agents for support, operations, finance, and reporting workflows, with guardrails and human oversight.
Need something that answers rather than acts? The chatbot answers questions; the agent resolves them.
See AI chatbot development →Ready to Build Something Great?
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