AI Module 03 AGENT FLOWS
When One Agent Isn't Enough
Real work has steps. Research, then draft, then check, then get approval, then file it in three systems. Cramming all of that into one giant prompt is how you get an agent that's mediocre at everything. We design agent flows instead: specialized agents that plan, divide, hand off, and review each other's work, with clear rules for who does what and what happens when something breaks.
Choreography With A Referee
Planners, Workers, Reviewers
A planner breaks the job down, workers handle the pieces in parallel, and a reviewer checks the output against your rules before anything leaves the building. It's the same pattern good teams use, just faster and without the meeting.
Routing & Handoffs
Simple requests take the cheap, fast path. Hard ones get escalated to bigger models or to a person. Every handoff carries the context it needs, so nobody, human or agent, has to ask "wait, what are we doing?"
Not Every Step Needs A Big Model
Fast Judgments With System One Models
A lot of what happens inside a flow isn't writing or reasoning. It's a judgment call: which queue does this ticket belong in, is this a refund request, how urgent is it, which of these handlers fits? For those steps we often reach for System One models like Jev. Instead of generating paragraphs, they return a typed answer with a calibrated probability, fast and cheap enough to run on every single message. Our code owns the workflow; the model supplies the common sense right where plain rules run out.
Knowing When Not To
They aren't the answer to everything, and we'll tell you when they're not. If a rule or a lookup can decide it, we just write the code. If the job is drafting a reply, summarizing a contract, or reasoning through a messy multi-step problem, that's work for a generative model. They read text only (for now), and a broad, fuzzy question gets split into a few narrow ones. We also test confidence thresholds on your real data before trusting them: confident answers flow straight through, and uncertain ones go up to a bigger model or a person.
Services Overview
- Labeling
- Classification
- Routing
- Confidence Thresholds
Built For The Long Haul
Some flows finish in seconds. Others wait on a vendor reply, a nightly data load, or a manager's approval on Monday. We build durable, background workflows that persist their state, resume where they left off, and never send the same email twice. Approval gates sit wherever your business needs a human in the loop.
Services Overview
- Background Jobs
- Approval Gates
- Durable State
Fails Loudly, Recovers Quietly
Models time out. APIs rate limit. Somebody renames a field. Our flows retry safely, fall back gracefully, cap their own spending, and leave a full trace of every step so you can replay exactly what happened. When something truly needs attention, the right person hears about it, and everyone else gets to keep their weekend.
Services Overview
- Retries
- Budgets
- Tracing
- Alerting
Diagram: a router sends simple requests to a small model and complex ones to a planner, which coordinates research and drafting agents and a reviewer, with optional human approval before completion.
Great flows feel like a well-run kitchen. Everyone knows their station, and nothing leaves without a taste test.
Contact us for more info!!
Frequently Asked QUESTIONS
When do we need multiple agents instead of one?
When the work has distinct steps, needs different tools or permissions at each step, benefits from parallel work, or needs a separate check before anything is final. If one focused agent will do, we'll tell you.
Can agent flows include human approvals?
Yes. Approval gates can pause a flow for as long as needed and resume exactly where it left off once someone signs off.
How do you keep multi-agent costs under control?
Routing cheap tasks to smaller models, caching, per-run budgets, and tracing that shows exactly which step costs what.
Do you use smaller, faster models for routing and classification?
Yes, when they fit. System One models like Jev return typed answers with calibrated confidence for labeling, classification, and routing decisions, at a fraction of the cost and latency of a generative model. We test them on your data, set thresholds from the results, and send low-confidence cases to a larger model or a person.
Can you connect flows to our existing systems?
That's the point. We typically connect flows to your tools through MCP servers or direct APIs, with least-privilege access for each agent.