How ShipStation Global built an AI IT help desk on Amazon Bedrock AgentCore

來源原標題

How ShipStation Global built an AI IT help desk on Amazon Bedrock AgentCore

作者

Kenneth Salmon

原始標籤

Advanced (300) · Amazon Bedrock AgentCore · Customer Solutions

原始連結

https://aws.amazon.com/blogs/machine-learning/how-shipstation-global-built-an-ai-it-help-desk-on-amazon-bedrock-agentcore/

摘要原文

This post is co-written with Kenneth Salmon, Edward Boswell, Jack Armstrong, Robert Bahn, and Nick Ciubotariu from ShipStation Global. Answering the same IT questions and clearing routine tickets drains time that IT teams could spend elsewhere. To reclaim it, the ShipStation Global Engineering team built Deckhand , an AI-powered IT help desk agent for Slack, on Amazon Bedrock AgentCore . ShipStation Global is the leading intelligent shipping and logistics platform for small and mid-sized shippers, and it operates on a simple belief: the more efficient its teams are, the better it delivers for customers. Deckhand puts that belief to work. It answers routine IT and company questions from a curated knowledge base, reads error-message screenshots, and creates and updates Jira Service Management tickets. In this post, we discuss how ShipStation architected Deckhand, which resulted in resolving 97.5 percent of incoming channel queries while earning a 96 percent positive feedback rating by employees (Figure 1). Figure 1: 3-month channel coverage and positive feedback The problem Deckhand solves is the one every IT team will recognize: the same questions are routinely asked, error messages are posted after business hours, and information needs to be gathered from different teams. At ShipStation Global, those conversations lived in a busy help desk Slack channel, where employees asked, and IT answered, one thread at a time. The answers usually already existed in a Confluence how-to article, on a company intranet page, or in a thread from eight months earlier. Finding them was the hard part. The channel data made the scale concrete. In the 12 months before launch: “We created Deckhand because getting help should be as simple as asking a question in Slack, not deciphering a ticket form or sending an email into the void. We believe that tickets are an indication of operational friction not productivity. Deckhand resolves common questions on the spot and, when it can’t, converts the conversation into a fully contextualized ticket with one click, improving the employee experience and freeing our team from endlessly repeating the phrase: ‘Please file a ticket.’” — Chris Collins, Senior Director IT at ShipStation Global Deckhand works in Slack like any teammate—joining channels, reading the conversation, and drawing context from Slack profiles to answer IT questions in place. We run it on Amazon Bedrock AgentCore, a platform to build, connect, and optimize agents at scale, with any framework or model. AgentCore runtime, a capability of Amazon Bedrock AgentCore, hosts the agent in isolated sessions, so we run no servers or containers. The solution has four main components, shown in Figure 2: Figure 2: Architecture diagram showing Slack events entering the Slack service and Amazon Bedrock AgentCore runtime, with separate paths to an Amazon Bedrock knowledge base, AgentCore Gateway-backed Lambda tools, Jira Service Management, and DynamoDB feedback. When an employee posts in a channel where Deckhand is a member, the Slack service assembles the full thread history and user context. It then invokes the agent through the AgentCore runtime API. The agent searches the knowledge base and calls tools as needed. When it finds an answer, it returns a final response (Figure 3). When it doesn’t, it asks a clarifying question or offers to create a ticket (Figure 4). Figure 3: Deckhand responds with self-service steps Figure 4: Deckhand offers to create a ticket This section walks through how Deckhand is implemented, from prerequisites to a working deployment. To implement this pattern, you need the following: The agent is intentionally lightweight. Using the open source Strands Agents SDK , an agent consists of a model, a set of tools, and a system prompt. The SDK manages the reasoning loop, allowing the model to select tools, process results, and iterate until it produces an answer. Deckhand’s agent service is implemented in a few lines of Python. The model ID is set through an environment variable, which allows switching models without modifying application code. Tools are integrated with the agent in two ways. Amazon Bedrock Knowledge Bases retrieval is a native Strands tool that supports knowledge base search with a single import. Other functions, including classification, ticket management, user resolution, and image analysis, are handled by a Lambda function registered on AgentCore Gateway. The Gateway exposes these as MCP tools, and the Lambda routes each invocation by tool name. This separation allows tool logic to be updated independently, and the agent can access new tools from the Gateway without requiring a rebuild. AgentCore runtime hosts the agent as a container, avoiding the need for server or session management. The Slack service calls the agent synchronously for each message, providing the thread transcript and user context, and posts the final response. Since context, including the employee’s profile, is included with every request, the agent can complete ticket details automatically. Deckhand answers from a single Amazon Bedrock knowledge base with core S3-backed data sources: Each document includes metadata such as title, source URL, content type, and event dates. This allows the agent to filter content, for example, when users ask about upcoming events or weekly lunch menus in the Austin office. The most valuable data source is the archive of resolved support threads. Before Deckhand existed, Slack was where problems got solved: years of employees describing issues and IT walking them through fixes. That history is an organic knowledge base, validated by the best possible reviewer: the person whose problem got fixed. It’s also noisy: a raw export mixes banter, automated alerts, and threads that never actually got resolved. Indexing an unverified fix or a garbled summary means the knowledge base surfaces an incorrect answer with the same confidence as a correct one, so extraction needs to be precise. The mining process follows a three-stage pipeline (Figure 5): Figure 5: Export classification and extraction workflow of the Slack conversation We chose RLM because it maintains the entire Slack history in a persistent sandbox variable space, allowing iterative reasoning over the export and consistent output structure. As a result, when an employee encounters a problem previously solved by a colleague, Deckhand provides the proven solution, often as actionable, numbered steps. An AI bot in a shared channel fails socially before it fails technically. Deckhand’s most refined logic decides when to stay quiet: Each answer includes feedback buttons. A thumbs-up is recorded directly, while a thumbs-down prompts a brief “what went wrong” field. Ratings are stored in DynamoDB with the original question and the bot’s response. Negative feedback is a valuable dataset used to improve the system prompt and agent behavior, and to update incorrect knowledge base information. Prompt rules enhance behavior but do not replace access controls. Enforcement is managed through code and infrastructure: Deckhand launched in April 2026. It started at about 30 messages a day and reached as many as 100 by early summer. In its first three months it handled 1,935 messages, started 1,221 agent conversations, and opened 409 Jira tickets. It replied to 97.5 percent of messages. A third of the questions came in after hours, when no one on the IT team was working, and Deckhand answered 94 percent of those. People who would have waited until the next morning got an answer that night. Feedback improved as the team worked on it. Positive ratings went from 82 percent in April to 96 percent in June, after they revised the system prompt and knowledge base to fix what people complained about. The clearest win was ticket deflection. An agent conversation is a question that would otherwise have become a Jira ticket. Of the 1,221 conversations, only 409 turned into tickets.