Context
The work focused on designing robust agentic systems that could coordinate multiple agents, state, tasks, errors, and documentation.
AI & Agentic Systems
Agentic AI Architecture and Engineering
Enterprise multi-agent architecture for stateful workflows and human-in-the-loop control.
Agentic AI Systems · LangGraph / CrewAI / AutoGen + OpenAI / Anthropic / Gemini · International (USD)
Challenge
Agentic systems become brittle when orchestration, state, and human checkpoints are not designed upfront. The architecture needed operational clarity, not just prompt chains.
Solution
Avlys designed multi-agent architectures using LangGraph, CrewAI, AutoGen, OpenAI, Anthropic, and Gemini patterns with orchestration, HITL workflows, state-managed pipelines, error handling, and documentation.
Delivered.
Multi-agent orchestration
Human-in-the-loop workflows
State-managed pipelines
Error handling and documentation
Outcomes.
A clearer blueprint for production-grade agent systems.
Better separation of agent responsibilities and state.
Defined checkpoints for human review and error handling.
Reusable architecture patterns for advanced AI workflows.
How it was built.
01
Audited the data sources, workflows, and constraints the system had to respect.
02
Designed the agent architecture: orchestration, state, grounding, and human checkpoints.
03
Built the pipelines, integrations, and evaluation harness around real cases.
04
Reviewed accuracy and failure modes, then documented the architecture for extension.
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