Multi-agent orchestration and AI workflow automation

The hard part of an agentic system is rarely the model call. It is everything around it: malformed output, long jobs, write access to production, and failures nobody notices. For a plain-language definition, see what agentic AI is.

• The work divides. A long document, a batch of records or a queue of requests can run concurrently.
• Steps need different powers. One step only reads, another writes to an internal system.
• Quality is judged differently at each step. Drafting wants range; classifying wants consistency; a final check wants strictness.
• A person belongs in it. When a decision needs sign-off, the workflow pauses, presents the evidence and carries on.
• The process already has stages. If the team describes the work as a sequence of handovers, the agent architecture usually mirrors it.

We usually start with one well-scoped agent and split it only when a step proves it needs its own context, tools or review. Splitting early buys architecture diagrams; splitting late buys a system nobody can debug. That path is on the custom AI agent development page. A conversational front end sits on the agentic chat assistant page.

1. Map the workflow as it runs today, including manual steps and undocumented exceptions.
2. Split it into jobs with their own input, output and definition of done.
3. Decide what context each agent sees and which tools it may call.
4. Define each handoff as a structured payload rather than free text, so it can be validated.
5. Choose what runs in parallel and what stays sequential, then size concurrency to real volume.
6. Put evaluation and human review gates where a wrong answer is expensive, and logging everywhere.

Step six is covered on the closed-loop AI evaluation and agentic testing page.

• Internal software and APIs - the systems the process already runs on, so no migration is required.
• Documents - parsing and segmenting files so their contents become workable units.
• Data stores - retrieval and semantic search, covered on the vector databases and retrieval page.
• People - review gates, approvals and escalation paths where judgment is required.

Report Grader parses DOCX and PDF reports and segments them for parallel LLM calls. Over 1,000 token-efficient LLM calls run per report analysis. Skill-enabled agents map to a domain-specific corpus, so grading stays grounded in the review criteria. Human-in-the-loop grading lets an inspector contribute. The Report Grader product page walks through the architecture.

Build-a-Dress runs agentic AI workflow automation for quoting. It classifies dress complexity, weight and other pricing signals, then prices the garment automatically. Orders route to verified manufacturers through a verification and payout setup. The application supports over 2,000 monthly active users. The Build-a-Dress product page covers the quoting workflow and the payment integration.