## Overview AutoScout24 embarked on a strategic initiative to transform scattered generative AI experiments into a cohesive, production-ready framework they call the "Bot Factory." This case study represents a mature approach to LLMOps that addresses a common challenge organizations face: moving from experimental AI projects to standardized, scalable production systems. The company partnered with the AWS Prototyping and Cloud Engineering (PACE) team for a three-week AI bootcamp to establish this framework, which demonstrates several important LLMOps principles including standardization, reusability, security, and operational observability. The Bot Factory concept is essentially a centralized framework for creating and deploying AI agents that can perform tasks and make decisions within workflows. The initial implementation targeted a specific pain point: internal developer support operations where platform engineers were spending up to 30% of their time on repetitive tasks such as answering documentation questions and granting tool access. This quantified business impact provides a clear justification for the investment in productionizing AI systems. ## Architecture and Technical Implementation The architecture demonstrates sophisticated LLMOps practices through its event-driven, serverless design built primarily on AWS managed services. The request flow follows a well-structured pattern that prioritizes security, resilience, and maintainability: **Ingestion and Security Layer:** User interactions begin in Slack, where requests are sent to an Amazon API Gateway endpoint. This triggers an AWS Lambda function that performs cryptographic signature verification to ensure requests are authentic. This security-first approach at the ingress point is a critical LLMOps practice, especially when exposing AI systems to users through third-party platforms. **Decoupling Through Queuing:** Verified requests are placed onto an Amazon SQS FIFO (First-In, First-Out) queue. This architectural decision provides several LLMOps benefits. First, it decouples the front-end from the agent execution layer, improving overall system resilience and allowing components to scale independently. Second, the use of FIFO queues with the message's thread timestamp as the MessageGroupId ensures that messages within a single conversation are processed in order, which is essential for maintaining coherent multi-turn interactions - a common requirement in production AI agent systems. **Agent Execution Runtime:** The core agent execution happens in Amazon Bedrock AgentCore, which provides a fully managed, serverless runtime environment. This is where the LLMOps value proposition becomes clear. Rather than building custom infrastructure for session management, container orchestration, security isolation, and scaling, AutoScout24 leverages a purpose-built managed service. This allows their engineering team to focus on agent business logic rather than undifferentiated infrastructure work. **Session Management Strategy:** A particularly noteworthy implementation detail is the session management approach. The system generates a unique, deterministic sessionId for each Slack thread by combining the channel ID and thread timestamp. This sessionId is passed with every agent invocation within that thread, enabling AgentCore to maintain conversational context automatically. Different threads receive different sessionIds, ensuring complete isolation between conversations. This demonstrates mature thinking about stateful AI applications in production - each conversation runs in an isolated session with its own resources, preventing context leakage between interactions while enabling natural multi-turn conversations within a single thread. ## Multi-Agent Architecture Pattern The solution employs an "agents-as-tools" design pattern that represents advanced LLMOps architecture thinking. Rather than building a monolithic agent that tries to handle all possible tasks, the system uses a central orchestrator agent that intelligently delegates requests to specialized, single-purpose agents. For the support bot use case, this includes: - A Knowledge Base agent for handling informational queries using RAG - A GitHub agent for executing actions like license assignments - The orchestrator agent that routes requests to the appropriate specialist This modular design provides several production benefits. New capabilities can be added by simply creating new specialized agents and registering them as tools with the orchestrator, without re-architecting the entire pipeline. The team mentions being able to add capabilities like a PR review agent in the future. This extensibility is a key characteristic of production-ready LLMOps systems. The multi-agent approach also enables model selection flexibility - a crucial cost and performance optimization strategy in production. More powerful (and expensive) foundation models can be assigned to complex reasoning tasks handled by the orchestrator, while lighter, more cost-efficient models can power routine worker agents handling straightforward operations like GitHub license requests. This ability to mix and match models across the agent architecture allows organizations to balance cost, performance, and accuracy, which is a sophisticated LLMOps optimization strategy. ## Development Framework and Code Structure The implementation leverages the Strands Agents SDK, an open-source framework that simplifies defining agent logic, tools, and behavior in Python. The case study provides code examples showing how the orchestrator agent can be defined in just a few dozen lines of declarative code. The framework uses a model-driven approach where developers focus on defining the agent's instructions and tools, while the foundation model handles reasoning and planning. The code structure demonstrates clean separation of concerns. The developer writes: - Model selection (pointing to specific foundation models in Amazon Bedrock) - System prompts that define agent behavior and instructions - Tools lists that provide agent capabilities (which in this architecture represent entry points to specialized agents) The AgentCore runtime then handles all the operational complexity including scaling, security isolation, and execution. This abstraction level is significant from an LLMOps perspective because it dramatically lowers the barrier to building production-grade agents. The example shows how a GitHub Copilot license agent is implemented as a simple tool function using the @tool decorator, encapsulating the specialized agent logic. ## Security and Compliance Considerations The architecture incorporates several security best practices essential for production LLMOps. Each agent invocation runs in its own isolated container through AgentCore, helping prevent data leakage between sessions. Agents are assigned specific IAM roles following the principle of least privilege to restrict AWS permissions. Sensitive credentials like GitHub API keys are stored in AWS Secrets Manager and accessed at runtime rather than being hardcoded or stored insecurely. The case study mentions that while the architectural diagram shows the core generative AI workflow, a production environment would integrate additional AWS services including IAM, CloudWatch, X-Ray, CloudTrail, WAF, and KMS to enhance security, observability, and operational governance. This acknowledgment shows mature understanding of production requirements beyond just getting the AI functionality working. ## Observability and Monitoring The session management strategy directly supports observability, which is a critical but often overlooked aspect of LLMOps. Each interaction can be traced using AWS X-Ray based on the unique sessionId, providing end-to-end visibility from the Slack message arriving at API Gateway through the entire processing pipeline: message enqueuing in SQS, orchestrator processing, foundation model invocation, tool calls (knowledge base lookups or GitHub API calls), and response delivery back to Slack. The distributed tracing includes metadata and timing information that helps teams understand where time is spent in the agent execution flow. If a step fails or experiences latency issues (such as a timeout on an external API call), X-Ray can pinpoint exactly which component caused the problem. This level of observability is invaluable for operating AI agents in production, where debugging distributed asynchronous systems can otherwise be extremely challenging. ## Deployment and Production Readiness The solution demonstrates a clear path from prototype to production. The team moved from experimentation to a functional Slack bot actively handling production workload within a three-week bootcamp period. This rapid timeline was enabled by leveraging managed services rather than building infrastructure from scratch. The serverless architecture using Lambda, SQS, and AgentCore provides automatic scaling without requiring capacity planning or infrastructure management. The event-driven design naturally handles variable workloads, which is typical for support bot usage patterns that may experience peaks and quiet periods. ## Outcomes and Business Impact The project delivered both immediate tactical value and strategic capabilities: **Immediate Value:** A production-ready support bot is actively reducing the manual support burden that was consuming 30% of the AI Platform Engineering Team's time. This quantified productivity improvement provides clear ROI. **Strategic Foundation:** More importantly, the project established a validated, reusable architectural pattern - the Bot Factory blueprint. Now any team at AutoScout24 can build a new agent by starting with this proven template (Slack -> API Gateway -> SQS -> AgentCore) and focusing on their unique business logic rather than infrastructure concerns. This standardization significantly accelerates innovation cycles across the organization. **Democratization of AI Development:** By abstracting infrastructure complexity, the Bot Factory enables domain experts (in security, data analytics, etc.) to create new tools and specialized agents without needing deep expertise in distributed systems. This democratization effect is a key benefit of mature LLMOps platforms. ## Critical Assessment and Balanced Perspective While this case study presents an impressive implementation, it's important to maintain critical perspective since the content originates from AWS promotional material: **Vendor Lock-in Considerations:** The architecture is deeply tied to AWS services (Bedrock, AgentCore, Lambda, SQS, API Gateway, Secrets Manager, X-Ray). While this provides convenience and integration benefits, it also creates significant vendor lock-in. Organizations considering this approach should evaluate portability concerns and the long-term implications of building core AI infrastructure on proprietary managed services. **Cost Implications:** The case study doesn't discuss operational costs. While the architecture enables model optimization strategies (using lighter models for simple tasks), the overall cost structure of running agents through managed services like Bedrock AgentCore versus self-hosted alternatives isn't addressed. The serverless approach can be cost-effective at certain scales but may become expensive at high volumes. **Complexity Trade-offs:** While the multi-agent architecture provides modularity and extensibility, it also introduces complexity. Debugging interactions between multiple agents can be challenging even with good observability tools. The orchestration logic adds latency and potential failure points compared to simpler single-agent architectures. **Limited Performance Data:** The case study doesn't provide specific performance metrics such as response latency, accuracy rates, user satisfaction scores, or concrete productivity improvements beyond the initial "30% of time" problem statement. Without these metrics, it's difficult to assess the actual production effectiveness. **Maturity and Scale Questions:** The implementation appears relatively recent, and we don't know how it performs at scale or with more complex use cases. The initial use case (support bot) is relatively straightforward compared to more demanding agent applications. **Framework Dependencies:** The reliance on the Strands Agents SDK introduces another dependency. While open-source, the long-term maintenance, community support, and evolution of this framework could impact the solution's sustainability. Despite these considerations, the case study demonstrates several commendable LLMOps practices including proper security implementation, thoughtful session management, comprehensive observability, and architectural patterns that promote reusability and standardization. The focus on creating a reusable blueprint rather than just solving a single use case shows strategic thinking about scaling AI capabilities across an organization. The multi-agent pattern with specialized agents and intelligent orchestration represents a sophisticated approach that balances flexibility with maintainability.