AI frameworks and agent build
A support agent that reads your product's code, answers what it can, and knows when to ask a person
Generic helpdesk bots answer generic questions. Ours is grounded in your own codebase and documentation, so it can answer questions about your software, your builds and your integrations, and it escalates to a person with its reasoning attached when its confidence is below the threshold you set.
Any business that supports its own software
A SaaS product, an internal platform, a portfolio of bespoke client builds, an ecommerce store with custom integrations: the answers to most support tickets are in a codebase and a documentation store that no off-the-shelf chatbot has ever read. So tickets wait for the one person who knows, simple questions queue behind complex ones, and developers spend their time on triage rather than fixing.
The support-agent framework exists for exactly that situation. It runs in production on our own group's helpdesk at Agency51, a BigCommerce agency, where it triages around a hundred tickets a week across dozens of bespoke builds.
Classify, retrieve, draft, score, act
Classify
Configuration question, bug report, feature request, billing, or something it cannot place. Classification drives which knowledge is retrieved and which escalation rules apply.
Retrieve
The relevant code, configuration and documentation for that product or client, indexed so retrieval is grounded in the actual build, not a generic manual.
Draft
A reply in your tone with the specific steps or explanation for that build, with the sources it used attached.
Score
Confidence from retrieval quality, classifier agreement and self- checks against the draft, tuned on an evaluation set of real historical tickets with known-good answers.
Act
Above the threshold, the reply goes out and the ticket is tagged for light review. Below it, a person gets the ticket with the draft, the reasoning and the code the agent looked at, so they start from a head start rather than from nothing.
Built in from the first scope
Evaluation harness
A set of real historical tickets with agreed answers, run on every change to the agent, with resolution accuracy and escalation rate reported weekly. An agent that cannot be measured is not ready for production.
Confidence-based escalation
A tunable threshold, set conservatively at launch and reviewed as the evaluation set grows. Different classes of ticket can carry different thresholds.
Helpdesk integration
Works inside the ticketing system you already use; nothing changes for customers except how quickly they hear back.
Human in the loop
Every escalated case is reviewed by a person, and every review feeds the evaluation set, so the agent gets better on the tickets it got wrong.
Transparency
Customers are told an AI assistant drafts first responses and a person is always reachable, which is both good practice and the EU AI Act's limited-risk transparency duty.
Audit trail
Every decision logged with the sources used and the confidence score, for review, compliance and improvement.
~100Tickets a week triaged in production at Agency51
Hours → minutesFirst-response SLA before and after
1Framework, tailored to each client's own code and workflow
Weeks to a production pilot
One to two weeks of scoping: the knowledge sources, the helpdesk integration, the ticket classes, the escalation rules and the evaluation set, agreed before any code. Three to six weeks of build, which is mostly connecting the framework to your repositories, documentation and ticketing system and tuning it against the evaluation set. Then a bounded production pilot with real traffic and a person reviewing every escalation. The code is yours; afterwards, a handover with training or a managed service, your choice.
About support agents
Which helpdesks does it work with?
Any with an API: Zendesk, Freshdesk, HubSpot, Jira Service Management, Intercom, or a bespoke queue. The framework sits beside the helpdesk rather than replacing it.
Can it really read our code?
Yes. Repositories are indexed alongside documentation so retrieval can reach the actual implementation. At Agency51 that means answering questions about a client's bespoke BigCommerce checkout by reading the theme and app code, not a generic BigCommerce manual.
What stops it making things up?
Grounding, scoring and the threshold. Replies cite the sources they used; confidence is scored against those sources; and anything below the threshold goes to a person. The evaluation set catches regressions before they reach customers.
Which model does it use?
The Agency51 deployment runs on Claude within our framework. The framework itself is model-agnostic: the model for each deployment is chosen on the evaluation set, data terms, cost and latency, and a change of model is a configuration change, not a rebuild.
What does it cost to run?
Model usage is metered per ticket and is usually a small fraction of the developer time it saves. We report cost per interaction weekly alongside the resolution and escalation rates.
Give your developers the first twenty minutes of every ticket back
Tell us what your ticket queue looks like and where the answers live.