Build AI agents for business intelligence with Amazon Bedrock AgentCore

OPLOG, a technology-driven fulfillment company powered by AI and robotics, processes millions of items monthly across Türkiye, the United Kingdom, and Germany for major brands and global marketplaces. Operating a customer-agnostic fulfillment model where multiple brands share warehouse infrastructure, workers, and autonomous robots, OPLOG faced a challenge common to many B2B organizations: fragmented business data across systems resulted in delayed insights and manual reporting that consumed hours of productive time daily.

To address this challenge, OPLOG built a production-ready business intelligence (BI) system using AI agents deployed on Amazon Bedrock AgentCore. The solution processes business transactions autonomously, delivering real-time intelligence across sales pipeline management, data quality enforcement, and prospect research. The results demonstrate measurable business impact: 35% reduction in sales cycles, 91% improvement in CRM data completeness, and 98% reduction in manual research time.

In this post, we show you how OPLOG developed three AI agents using the Strands Agents SDK, deployed them to Amazon Bedrock AgentCore, and integrated Amazon Bedrock with Anthropic’s Claude Sonnet and Amazon Bedrock Knowledge Bases for Retrieval(RAG). We describe the architecture, implementation approach, and business outcomes that demonstrate how AI agents can transform BI operations.

OPLOG’s business and data challenges

OPLOG’s rapid growth created operational complexity that traditional BI systems couldn’t address. The company’s data existed across multiple disconnected systems: Hubspot CRM contained sales pipeline information, communication systems stored customer conversations, Microsoft Teams held communication context, and Databricks warehouses maintained operational metrics. Each system operated independently, creating data silos that prevented comprehensive BI.

The fragmentation created specific operational pain points. 2 accessing reports from different systems, synthesizing information, and preparing updates. This manual process meant insights arrived too late—weekly reports missed 60% of opportunities because deals had already progressed or stalled by the time analysis was complete. CRM data quality suffered as sales representatives, overwhelmed by manual data entry requirements, entered information inconsistently. Operations teams detected issues hours after they occurred, forcing reactive responses rather than proactive intervention.

OPLOG quantified significant operational costs from fragmented BI—including lost opportunities from delayed insights, manual reporting overhead consuming productive time, inconsistent data quality impacting decisions, and reactive operations forcing inefficient responses. The company needed a solution that could autonomously process data across the systems, deliver real-time intelligence, and remove manual reporting overhead while maintaining data quality and enabling proactive decision-making.

Solution overview

OPLOG developed three AI agents, each focused on a specific BI domain. The agents operate independently without communicating with each other; each processes data from specific sources and delivers targeted intelligence:

• Deal Analyzer Agent – This agent executes on a scheduled basis aligned with business operations, analyzing the Hubspot deals with recent activity. It validates deals against OPLOG’s sales methodology, identifies missing fields, and reports completion status to Microsoft Teams. The agent facilitates sales pipeline data quality and methodology conformance through automated daily reporting.
• Sales Coach Agent – This agent responds to Hubspot webhook events when deal stages change, validating required fields based on OPLOG’s business model (B2C only, B2B only, or B2B and B2C), and automatically creating tasks for missing information. The agent enforces data quality standards in real time, helping prevent deals from advancing with incomplete data.
• Lead Insight Agent – This agent triggers when new marketing leads are added to Hubspot, analyzing the lead’s digital presence across six social media environments (Instagram, LinkedIn, Facebook, YouTube, Twitter, TikTok). It applies OPLOG’s qualification methodology to assess Ideal Customer Profile (ICP) fit, compiles comprehensive profiles with fit determination, and delivers research reports to Microsoft Teams, minimizing manual prospect research while focusing sales energy on high-potential opportunities.

The architecture uses Amazon Bedrock AgentCore as the deployment environment for the agents. OPLOG developed agents using the Strands Agents SDK, which provides the framework for defining agent behavior, custom tools, and integration points. Each agent uses Amazon Bedrock with Anthropic’s Claude Sonnet for inference—analyzing data, reasoning through business rules, and generating insights. Amazon Bedrock Knowledge Bases implements RAG, allowing agents to retrieve relevant context from sales playbooks, product catalogs, and methodology documents stored in Amazon Simple Storage Service (Amazon S3).

AWS Lambda functions handle external system integrations, connecting agents to Hubspot, Microsoft Teams, and external data sources. Amazon EventBridge schedules agent executions for the Deal Analyzer Agent, and Hubspot webhooks trigger the Sales Coach and Lead Insight Agents in real time. AgentCore Observability provides comprehensive monitoring, tracking agent invocations, performance metrics, and costs through Amazon CloudWatch.OPLOG pays only for agent executions, with no infrastructure to manage. AgentCore Runtime scales automatically from zero to thousands of sessions based on workload, and deployment updates happen without downtime.

The following sections detail how OPLOG implemented each agent to address specific BI challenges. The Deal Analyzer Agent provides scheduled pipeline reporting, the Sales Coach Agent enforces real-time data quality, and the Lead Insight Agent automates prospect research. Although each agent serves a distinct purpose, they share a common technical foundation built on Amazon Bedrock, Amazon Bedrock Knowledge Bases, and the Strands Agents SDK, all deployed to Amazon Bedrock AgentCore.

Deal Analyzer Agent: Daily pipeline quality reporting

Sales managers at OPLOG faced a daily challenge: reviewing dozens of deals to identify which ones had missing information. Manual review took hours and often missed issues until deals stalled. The Deal Analyzer Agent helps solve this by running automated analysis on a scheduled basis, delivering comprehensive reports to Microsoft Teams that highlight exactly which deals need attention.

The following diagram illustrates the agent architecture:

EventBridge triggers Lambda on a schedule aligned with business operations. Lambda invokes AgentCore Runtime, which executes the agent to analyze the Hubspot deals with recent activity. The agent validates them against OPLOG Way methodology and sends formatted reports to Microsoft Teams.

OPLOG built the agent using the Strands Agents SDK with three specialized tools. The hubspot_properties() tool retrieves deal data and metadata from Hubspot’s API through Lambda. The deal_enrichment() tool performs the validation logic, analyzing deals against OPLOG Way methodology with business model-specific rules. The send_teams() tool formats results into structured reports and delivers them using webhooks. See the following code:

from strands_agents import Agent, tool
class DealAnalyzerAgent(Agent):
    @tool
    def hubspot_properties(self, deal_id: str) -> dict:
        """Retrieve deal data and metadata from Hubspot"""
        pass
    
    @tool
    def deal_enrichment(self, deal_data: dict) -> dict:
        """Analyze deal against OPLOG Way methodology"""
        pass
    
    @tool
    def send_teams(self, report: dict) -> bool:
        """Format and deliver report to Microsoft Teams"""
        pass

The validation logic handles OPLOG’s customer-agnostic fulfillment model complexity. Different deals require different validation based on whether they’re B2C only, B2B only, or B2B and B2C. For B2C deals, the agent validates B2C-specific fields plus the required fields. For B2B deals, it validates B2B-specific fields. For combined deals, it validates both fields. Conditional logic applies throughout—volume validation requires at least one inventory volume type for B2C deals, but requires both outbound and inventory volumes for B2B deals.

The agent uses Amazon Bedrock with Anthropic’s Claude Sonnet to interpret business rules and distinguish between intentionally zero values and missing fields—a nuanced decision that requires reasoning beyond simple null checks. Amazon Bedrock Knowledge Bases stores OPLOG Way methodology in Amazon S3 using industry-standard embedding models and vector databases. When validating deals, the agent queries the knowledge base with natural language, and Anthropic’s Claude applies the retrieved context to determine correct validation rules for each deal’s stage and business model.

Reports delivered to Microsoft Teams include deal completion status, missing field details, priority rankings, and actionable recommendations. Sales managers start their day with a clear view of which deals need attention. The implementation removed significant manual daily review time and improved stage accuracy by 91%. AgentCore Observability tracks processing time and report delivery success through CloudWatch.

Sales Coach Agent: Real-time validation and task automation

The Sales Coach Agent takes a different approach than the Deal Analyzer Agent—instead of reporting on issues, it enforces data quality in real time. When sales representatives move deals between stages, the agent immediately validates required fields and creates tasks for missing information. This helps prevent deals from advancing with incomplete data, making sure the pipeline stays clean.

The following diagram illustrates the agent architecture:The architecture uses Hubspot webhooks to trigger Lambda the moment deal stages change. Lambda invokes AgentCore Runtime, which validates the deal and creates tasks if needed—all within 10 seconds. This webhook-based approach means sales representatives can get immediate feedback when they try to progress deals.The agent uses two tools built with the Strands Agents SDK. The analyze_deal_properties() tool retrieves deal data from Hubspot and validates required fields based on the deal’s operating model and new stage. The assign_task() tool creates high-priority tasks with detailed instructions, links them to the deal, and assigns them to the deal owner.

See the following code:

from strands_agents import Agent, tool
class SalesCoachAgent(Agent):
    @tool
    def analyze_deal_properties(self, deal_id: str) -> dict:
        """Validate required fields based on operating model"""
        pass
    
    @tool
    def assign_task(self, deal_id: str, task_description: str) -> bool:
        """Create and assign validation task to deal owner"""
        pass

The validation logic mirrors the Deal Analyzer Agent’s business model rules but operates on a single deal in real time rather than batch processing. The agent uses the same Amazon Bedrock knowledge base that stores OPLOG Way methodology, querying it to determine which fields are required for the specific stage and business model combination. Anthropic’s Claude Sonnet interprets these rules and makes the critical distinction between intentionally zero values and missing fields.

Task descriptions are specific and actionable. Instead of generic “complete missing fields” messages, tasks specify exactly which fields need completion, why they’re required for the current stage, and guidance on how to complete them. This clarity helps sales representatives resolve issues quickly without needing to consult documentation or ask managers.

The implementation improved deal quality by 91% and achieved over 96% field completion. Response time averages under 10 seconds from stage change to task creation, with over 99.2% task creation success and over 97% validation accuracy monitored through CloudWatch.

Lead Insight Agent: Automated prospect research

Sales representatives at OPLOG used to spend significant time researching each new prospect—manually searching LinkedIn, checking company websites, reviewing social media presence, and trying to understand the business model. The Lead Insight Agent automates this entire process, helping deliver comprehensive profiles within 2–5 minutes of a new contact being added to Hubspot.

The following diagram illustrates the agent architecture:

The architecture uses Hubspot webhooks to trigger Lambda when new contacts are added. Lambda invokes AgentCore Runtime with the contact details, and the agent searches six social media environments in parallel: Instagram, LinkedIn, Facebook, YouTube, Twitter, and TikTok. After analyzing the digital presence, it delivers a comprehensive report to Microsoft Teams.

The agent uses AgentCore Browser for social media discovery. AgentCore Browser handles web navigation, JavaScript rendering, and content extraction—alleviating the need for custom web scraping infrastructure. The agent provides search queries and URL patterns (for example, site:linkedin.com/in/ [name]  [company] for LinkedIn), and AgentCore Browser returns structured content from each environment. It’s maintained by AWS, handles anti-bot protections, and scales automatically with agent invocations.

What makes this agent valuable in addition to its data collection capabilities is its analysis. Amazon Bedrock with Anthropic’s Claude Sonnet analyzes the extracted content to identify relevant profiles, summarize digital presence, and generate personalized approach recommendations. The agent applies OPLOG’s qualification methodology to assess ICP fit, determining whether the lead matches OPLOG’s target customer characteristics based on business model, industry, and digital footprint.

This ICP assessment changes how sales teams work. Instead of treating leads equally, they can prioritize high-potential opportunities. Reports include social media presence across the six environments, content analysis showing what the prospect shares and discusses, business model insights derived from their digital footprint, ICP fit determination with reasoning, and next-step recommendations for personalized outreach.

The implementation reduced prospect research time by 98%, while providing more comprehensive intelligence than manual research. The agent achieves over 92% social media discovery success and over 88% website accessibility. Sales teams report higher engagement rates on initial outreach because they have relevant context before making contact. AgentCore Observability tracks analysis time, coverage, and Teams delivery success (over 99.5%) through CloudWatch.

Business impact and technical outcomes

Sales performance improved significantly. Average deal cycles decreased by 35%. Lead conversion rates increased by 28%. CRM data completeness improved from 102%. Daily reporting time decreased by 92%. Sales representative productivity increased by 40%.

Operational efficiency gains were equally substantial. Issue detection time decreased by 81%. Resolution response time improved by 83%. Process compliance increased by 52%. Decision-making speed accelerated by 70%.

Technical performance metrics demonstrate production-grade reliability. The system delivers near real-time performance with 99.9% availability. The system processes thousands of daily business events across the agents. Cost-efficiency is achieved through serverless architecture that scales with usage, with infrastructure costs significantly lower than traditional systems.

The operational efficiency improvements delivered measurable ROI significantly exceeding the infrastructure costs of the AI agent system.

Conclusion

OPLOG’s implementation demonstrates how AI agents deployed on Amazon Bedrock AgentCore can transform BI operations. The system processes thousands of daily business transactions autonomously, delivering 35% faster sales cycles, 92% reporting time reduction, and 99.9% uptime. The cost-effectiveness of serverless architecture—representing significant reduction compared to traditional infrastructure—makes advanced AI-driven BI accessible and scalable.

“We believed AI could transform commercial operations entirely. With Amazon Bedrock AgentCore as our foundation, we’re not just improving sales cycles — we’re redefining how fulfillment companies compete at scale.” says Halit Develioğlu, Founder & CEO, OPLOG.

The solution’s success stems from several architectural decisions: using Amazon Bedrock AgentCore for agent deployment removes infrastructure management overhead; implementing RAG with Amazon Bedrock Knowledge Bases separates business logic from agent code, enabling updates without redeployment; using Anthropic’s Claude Sonnet for inference provides the reasoning capabilities necessary for complex business rule interpretation; and integrating EventBridge for scheduling and event-driven triggers enables both automated and real-time agent execution.

OPLOG continues to expand the system with additional agents, multi-modal capabilities for processing images and documents, and custom fine-tuning to optimize agent behavior for specific business contexts. The company’s roadmap includes additional operational and commercial AI capabilities currently in development.

Organizations interested in building similar AI agent solutions can get started with Amazon Bedrock AgentCore by exploring the developer guide, experimenting with the Strands Agents SDK to prototype an agent for a specific business process, and deploying to AgentCore’s serverless runtime. The pay-per-execution model means teams can start small and scale as they validate results.

To learn more about Amazon Bedrock AgentCore, explore the Amazon Bedrock AgentCore Developer Guide. For information about building AI agents with the Strands Agents SDK, see the Strands documentation. To explore Amazon Bedrock Knowledge Bases for RAG implementations, refer to the Amazon Bedrock Knowledge Bases User Guide.

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