Agentic Workflows
Multi-agent AI systems that handle complex, multi-step business processes end-to-end. Not chatbots — autonomous pipelines that plan, act, verify, and iterate.
What Is an Agentic Workflow?
A traditional automation script is rigid — it follows a fixed sequence of steps. A chatbot answers questions. Neither can handle the messy, conditional, multi-system complexity of real business processes.
An agentic workflow is different. It uses one or more AI agents that:
Receives PDF invoice by email → extracts line items via Document AI → matches against purchase orders in ERP → flags discrepancies → routes for approval if over threshold → posts to accounting system → archives with metadata. Zero human touch for 94% of invoices.
Runs nightly → scrapes competitor pricing pages, job boards, press releases → cross-references against your product catalog → generates structured diff report → posts to Slack with analysis and recommended responses.
Frameworks We Deploy
Role-based multi-agent orchestration. Assign agents specific roles (Researcher, Writer, Analyst, Reviewer) with defined goals and tools. Agents collaborate to produce outputs no single agent could achieve. Ideal for document production, research pipelines, and content workflows.
Graph-based workflow orchestration from the LangChain ecosystem. Defines agent workflows as directed graphs with conditional branching, human-in-the-loop checkpoints, and persistent state. Production-grade — handles complex, long-running pipelines.
Long-horizon autonomous task execution. Given a high-level goal, it plans and executes without step-by-step human guidance. Best for exploratory tasks — research, discovery, and open-ended analysis where the path isn't fully known upfront.
Software engineering agent that writes code, runs tests, debugs failures, and submits pull requests. Trained for code tasks — ideal for automating repetitive development work like migrations, refactoring, and test generation.
Simulates a software company with distinct agent roles — product manager, architect, engineer, QA. Given a feature request, it produces a full software specification, architecture diagram, and working code.
When standard frameworks don't fit, we build custom orchestration layers. Python-native, tested, and integrated with your existing systems. Full source ownership for the client.
Service Offering
Case Study
A pharmaceutical distributor needed to continuously monitor 80+ active suppliers for GMP compliance — tracking regulatory body announcements, supplier certification expiry dates, and news of quality incidents. The manual process required a full-time analyst reviewing dozens of sources daily.
UNYGMS deployed a LangGraph-based multi-agent system with three specialized agents: a Monitor Agent that ingests regulatory feeds and news sources, a Classifier Agent that determines relevance and severity, and a Reporter Agent that generates structured risk alerts. The system runs on-premise on a single server, produces daily compliance reports, and surfaces critical alerts within 15 minutes of publication.