SEEBEYOND AI · EVALUATION AND ECONOMICS
Baseline the work. Test configuration changes. Understand quality, human effort, and cost before expanding your agentic workflow.
For development teams refining an agent, delivery agencies preparing a handover, and business owners deciding whether to pilot or scale. Start with one process and one decision.
AI optimization means improving how an AI system performs a defined business task, while accounting for the effort and cost needed to produce an acceptable outcome.
For an agentic workflow, the variables may include prompts, models, retrieval, tool use, routing, and human approval. A lower token bill alone does not demonstrate improvement: an inexpensive model can require more review, correction, or fallback work.
Our focus is evaluating agents and workflow economics. Here, AI optimization does not mean making your website appear in AI-generated search answers or training a custom foundation model.
FROM DEVELOPMENT TO EXPANSION
Define the unit of work and measure the current manual or agent-assisted process. Record first-pass and final quality, handling time, review, correction, costs, volume, and failure categories. Agree what an acceptable result looks like before tuning.
Compare configuration versions on matched development tasks. Inspect failures and regressions, refine the design, and validate a locked candidate on unseen cases. A configuration change needs new evidence, not a changed label.
After agreed validation and safety gates pass, begin with bounded exposure and human approval. Define the owner, eligible tasks, monitoring, stop conditions, and rollback before the pilot starts.
Collect actual quality, human effort, costs, and incidents in the approved environment. Turn permitted production failures into regression cases. Retest changes and preserve fresh validation data before another release.
Review evidence before increasing volume, adding intents or users, or granting more autonomy. New tasks may need a new baseline. Improvement on a small test set is not production-readiness assurance.
Change a configuration, test it, review failures, refine, validate, and release gradually. Business owners use the findings to improve, pilot, pause, or expand. The lifecycle is a delivery method; it is not a claim that the prototype automates every step.
| Measure | Question it answers | What to watch |
|---|---|---|
| First-pass and final acceptance | How often is the work acceptable before and after correction? | Keep human judgments, pending reviews, and failure severity explicit. |
| Review and correction effort | How much human work remains? | Include rejected attempts; do not assume saved minutes become cash savings. |
| Cost per accepted result | What does useful output cost? | Include model and tool charges, human effort, and recurring overhead. Report fallback separately. |
| Latency and operational outcomes | Does the workflow help in real operations? | Queue time, resolution, reopen rate, and customer outcomes need separate observation. |
| Regression and readiness checks | Did a change introduce new failures? | Inspect results by task category and critical failure type, not just an average score. |
LLM cost optimization can involve model selection, routing, context size, or caching. The right intervention depends on observed costs and failure causes. We do not assume cheaper inference improves total workflow economics.
Consider an agent that drafts support replies using approved policy and verified order evidence. Start by evaluating the existing process. Then compare a prompt-only design, a grounded agent, and a design that routes routine tasks to a smaller model.
A draft that claims a dispatched order was cancelled should fail review. A correct escalation may be an acceptable draft, but it is not the same as resolving the customer’s case. Include correction and fallback work when comparing costs.
SeeBeyond’s prepared customer-service demo uses synthetic data and simulated results. It illustrates why a low API bill can coexist with high review cost, and why an aggregate target does not replace unseen validation. It is not a customer case study or evidence of realized savings.
Our AI Evaluation Sprint focuses on an agreed workflow, acceptance criteria, failure analysis, and improvement priorities. The AI Spend & Economics Review examines baseline spend, human effort, and unit economics. Scoped implementation follows when the evidence and requirements support it.
We agree permitted data, reviewer responsibilities, delivery scope, fees, and acceptance criteria before work begins. Existing evaluation tools may already supply useful evidence; we assess the gap before proposing additional infrastructure.
The browser-local prototype compares recorded or imported results, manual cost assumptions, human acceptance, review and correction effort, and configuration economics. It supports sensitivity analysis and exports. It does not execute agents, deploy releases, provide automated judging, or monitor production. Version history, automated regression runs, provider adapters, and production monitoring are planned capabilities.
Yes. Begin with a baseline and compare reviewed configuration results while developing. Today’s prototype uses externally generated results; automatic execution and continuous-integration release gates are not connected.
An agentic workflow can fail through missing evidence, incorrect tool use, unsupported actions, routing, or human handoffs. Evaluation needs to reflect the whole task and its constraints, not just the model’s answer in isolation.
No. Compare measured quality and full costs, including human effort and fallback. Realized value also depends on implementation, maintenance, throughput, staffing, and operational outcomes.
Not necessarily. We first assess whether existing tools and records answer the business question. Any integration or adapter work is separately scoped; those integrations are not active in the current prototype.
A named workflow and decision, a process owner, an agreed acceptance rubric, a qualified reviewer, and access to permitted representative records and costs. Start with synthetic or appropriately de-identified records when exploring the prototype.
Establish a baseline, evaluate a development change, or define the evidence needed for a controlled pilot or expansion.
AI evaluation, economics, consulting, training, and downloadable software.
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