Lineate AI Agent Development | Custom Agentic AI on Amazon Bedrock

Lineate AI Agent Development builds intelligent, goal-driven AI agents on AWS that can reason, take action, and interact with users or business applications. Whether they support employees, customers or internal processes, we build custom agents around your unique business needs, with the guardrails enterprises require to trust them in production.

• Use Case Identification: We work with stakeholders to find high-value opportunities where agents can improve efficiency, automate tasks or sharpen decision-making, making sure we solve the right problem first.
• Workflow & System Analysis: We map existing processes, data sources and integrations to understand exactly how agents will interact with your systems and users. Getting this right early is what separates reliable agents from ones that break in production.
• Agent Architecture Design: We design each agent's capabilities, decision logic, knowledge sources, tools and integrations, built around your environment rather than a generic template. For multi-step work, we design multi-agent systems with role-specific agents and clearly defined handoffs.
• Built-in Guardrails: Each agent is designed following security best-practices, with least-privilege access, human approval where it matters, server-side blocks, isolation, and traceability.
• Development & Integration: We build the agent, its tools and its integrations with your applications, APIs and data through secure connectors and Model Context Protocol (MCP) servers, then validate it against your real workflows and agreed acceptance criteria.
• Monitoring & Optimization: We evaluate performance, monitor outcomes and refine agent behavior over time to keep it accurate, reliable and effective. As a software development company that cares about what happens after go-live, this step isn't optional.

• Multi-Agent Delivery Pipeline: Built a multi-agent system for a leading AdTech platform that takes tickets from analysis to pull request across 30+ repositories. Thirteen role-typed agents (analyst, architect, engineers, QA, reviewer, release manager) run with tool permissions enforced by the runtime, parallel back-end and front-end agents coordinate through a frozen API contract with automatic drift detection, and only three human approval gates remain in the flow. Tickets can advance unattended from the issue tracker, and test failures are classified and routed to the right owner automatically.
• Automated LLM Insights on Amazon Bedrock: For a hotel group with 80+ properties, an LLM pipeline on Amazon Bedrock generates improvement actions automatically as new guest reviews arrive by email, each linked to the reviews behind it with a 5 to 7% error rate, for under $50 per month to run. An Amazon Q Business proof of concept added a conversational interface over the same data.
• Agentic Workflow on an AWS Data Platform: For a supply-side advertising platform, 100% of data-platform features now ship through an agentic workflow built on 40+ playbooks, 30+ reusable skills and 19 one-command deployments. Agents read and act across the issue tracker, wiki, code repositories and data platform through MCP integrations, with chat and monitoring access kept read-only and every production release human-gated.
• Grounded Conversational Assistant: For a nationwide children's reading platform, we built an assistant that answers only from the publisher's catalog. A 3-step pipeline (answer validation, factual outline, age-appropriate response) tailors replies to three reading-age tiers for safety and reliability.
• Human-in-the-Loop Document Automation: For an online K-12 school network enrolling 20,000+ students a year, we went from kickoff to production in 10 weeks with an AI document reviewer embedded in the CRM. Reviewers approve by exception, decisions write back under each reviewer's own identity for a clean audit trail, and an LLM layer is planned for document classification and requirement matching.

• AdTech and media companies automating campaign, data and engineering workflows across complex platforms.
• FinTech and financial services firms automating onboarding, document review and operations under strict controls.
• Healthcare and HealthTech organizations that need agents with privacy controls and human oversight.
• SaaS and platform companies adding agentic assistants to their products for their own customers.
• Engineering organizations adopting agentic software delivery with real guardrails.