Why auto-AGENTS™

It’s all about intelligence per dollar.

In a high-volume contact center, every penny per contact is millions a year. auto-AGENTS™ deliver frontier-model intelligence at a fraction of the cost, resolving end to end, scoring 100% of interactions, and recursively self-improving so the cost per resolved contact keeps falling. More resolutions, fewer dollars, every single week.

Book a demo See the platform

Autonomous resolution

Cheaper inference · TokenTrim™

Lower cost / resolution*

Scored, not sampled

Inference latency

Intelligence / $ vs human*

*Illustrative, directional figures for a high-volume deployment, not a quoted benchmark.

The economics

Intelligence per dollar, the only metric that scales.

Every class of contact-center automation trades intelligence against cost. Cheap systems can’t resolve; smart systems cost a fortune. auto-AGENTS™ sit where nothing else does, frontier intelligence at the low end of the cost curve, and the self-improvement loop pushes it lower every week.

▲ The sweet spot · smart and cheap

What it costs at your volume

Resolved contacts / month1,000,000

50K1M2.5M5M / mo

And every week the RSI loop trims auto-AGENTS™ cost per resolution further.

Illustrative blended cost per resolved contact (handling + escalations to humans where systems can’t resolve). Directional, for modeling only, not a quoted price.

Every penny, every person

Cheaper per contact. Warmer per customer.

Lower cost isn’t colder service. auto-AGENTS™ resolve faster and more naturally, for the customer on the line and the team behind them.

Customer smiling on a resolved call — Resolved on the first call

Customer on a call outdoors — Natural, on-brand voice

Customer happy on the phone — 155+ languages, 24/7

The shift

Scripted bots deflect. auto-AGENTS™ resolve.

For a decade, contact-center "AI" meant decision trees, intent menus and deflection, systems that fail the moment a caller goes off-script. Frontier LLMs broke that ceiling. auto-AGENTS™ reason about what's actually being asked, take real action, and learn from the outcome.

Same call · two architectures"I was charged twice"

Legacy scripted bot decision tree

1Match utterance to a fixed intent menu

2No branch fits → fall to "I didn't get that"

3Read static FAQ; cannot touch billing

→Deflect to a human queue

Outcome: escalation · 0 actions taken · no learning

auto-AGENTS™ LLM + agentic + RSI

✓Reason over intent, account & policy in context

✓Plan: locate duplicate charge, verify, refund

✓Act over MCP, Stripe refund, CRM note, SMS

↻autoQA scores it; winning policy promoted

Outcome: resolved end-to-end · audited · feeds RSI

The moat · why it keeps getting cheaper

The loop that drives cost per resolution down, every week.

Any vendor can wrap an API around a frontier model. The moat is what happens after the call. In production, auto-AGENTS™ run a continuous reinforcement loop: autoQA scores 100% of interactions, winning policies are promoted, and prompts, routing and TokenTrim™ inference-compression retune themselves. Quality compounds; cost per resolution decays. Every change is gated and reversible.

1Resolve 2Measure · autoQA 100% 3Learn · promote winners 4Optimize · TokenTrim™

TokenTrim™

~10× cheaper inference

Inference-compression trims the tokens behind every interaction by roughly 10×, so frontier-model quality runs at production economics, and the loop keeps tuning it lower.

autoQA

100% evaluated

Every conversation is scored, not a 2% sample. Quality, compliance and intent accuracy become a live, learnable signal the loop optimizes against.

Governed

Gated & reversible

Self-improvement never goes rogue. Each promotion clears the adversarial eval suite and human approval, with an immutable audit trail and instant rollback.

How auto-AGENTS™ compares

Every other class makes you choose. auto-AGENTS™ doesn’t.

Compared by class, not by logo. Legacy IVR & rules, first-gen intent chatbots, human BPO, and single-model GenAI agents each win on either intelligence or cost, never both, and none of them improve on their own. auto-AGENTS™ is the only class that resolves end to end, scores 100% of interactions, runs at ~10× cheaper inference, and gets cheaper every week.

Capability	auto-AGENTS™	Single-model GenAI agents	First-gen chatbots	Legacy IVR & rules	Human BPO
Cost per resolved contact*	$0.30	$1.20	$3.40	$4.20	$6.00
Resolves end to end, not just deflects	✓	~	–	–	✓
Frontier reasoning on the long tail	✓	✓	–	–	✓
Agentic action across your systems (MCP · 170+)	✓	~	–	–	~
Scores 100% of interactions (autoQA)	✓	–	–	–	–
Gets cheaper every week (RSI)	✓	–	–	–	–
~10× cheaper inference (TokenTrim™)	✓	–	–	–	–
Scales instantly to volume spikes	✓	✓	✓	✓	–
Voice + chat + email · 155+ locales	✓	~	~	~	~

✓ native · ~ partial / varies · , not offered. Columns are classes of approach, not specific vendors; rows reflect typical class behavior. *Illustrative blended cost per resolved contact, directional only.

The unfair advantages

What you only get from auto-AGENTS™.

Parts of these exist in isolation across the market. Brought together as one governed, self-improving runtime, voice and chat, on your systems, they're ours alone.

01 · RSI

It improves itself in production

A closed resolve → measure → learn → optimize loop retunes prompts, routing and TokenTrim™ every week, gated by adversarial evals and human approval.

Others: retrained in manual batches, if at all

us: compounds automatically, weekly

02 · autoQA

Graded on 100% of interactions

Every conversation is scored for intent, policy and quality, turning compliance into a live, learnable signal, not a 2% sample reviewed after the fact.

Others: sample ~2%, mostly for humans

us: 100%, feeding the loop

03 · TokenTrim™

Frontier quality at production cost

Inference-compression cuts the tokens behind every interaction ~10×, so you run frontier models 24/7 without frontier bills, and the loop keeps driving it lower.

Others: pay full frontier token cost

us: ~10× cheaper, self-tuning

04 · The DNA

Built from conversations-to-intelligence

auto-AGENTS™ is built by Commerce.AI, the team that turned customer conversations into product & market intelligence, then brought LLMs to the contact center early. Every call resolves and teaches.

Others: automation-first, bolted on

us: intelligence-first, by origin

"We evaluated the whole category. auto-AGENTS™ was the only one that resolved end to end, and kept getting better on its own."

Head of CX Platform

Enterprise evaluation

Why teams switch

Bought a bot. Got a queue. Then found a system.

Most teams arrive after a single-assistant bot stalled at deflection. auto-AGENTS™ resolves the long tail end to end and improves week over week, so the curve bends the right way instead of plateauing.

What makes it autonomous

Reasoning, action, and evaluation, wired into one runtime.

Autonomy isn't a single feature. It's the model reasoning, acting over your systems, and grading itself, running together, in production, on every interaction.

01 · Frontier reasoning

It understands the ask, not just the keyword.

auto-AGENTS™ reason over intent, account state, policy and conversation history in real time, on frontier LLMs. Model-agnostic underneath: route to GPT-5.x, Gemini, Claude or on-prem Llama, switchable per flow. Off-script questions get reasoned answers, not "I didn't get that."

p95 inference latency

locales reasoned in

Reasoning tracelive · streamed

Intent

refund · dup-charge

Confidence

0.97

model grounded in account + policy · planning 3 tool calls

02 · Agentic action · MCP

It doesn't just talk. It acts on your systems.

Over the Model Context Protocol, auto-AGENTS™ read and write to your CRM, billing, scheduling and internal tools, and dispatch specialist sub-agents during and after the interaction. 170+ connectors, a headless API, every action run inside your policies, fully audited and reversible.

See voice agents →

Actions · MCP bus3 tools · resolved

✓Stripe · refund $48.00200 OK

✓CRM · log resolution + notesynced

✓Twilio · SMS confirmationdelivered

03 · Self-evaluation

It grades itself before, and after, it ships.

Every build is run against generated adversarial scenarios, the CQB eval suite, scoring intent accuracy, latency, hallucination rate and policy adherence. In production, autoQA scores 100% of live interactions, feeding the RSI loop. The agent fixes its own regressions, gated by human approval.

interactions scored

eval scenarios / build

autoQA coveragelast 1,000 calls

100%

scored

avg autoQA

0.4%

halluc. rate

✓Refund under thresholdpassed

✓Unclear answer · recoversfix saved

✓Escalate over policy caphuman approved

Why it matters

The business case writes itself.

Autonomy isn't an experiment. It's measurable outcomes, resolution up, cost down, quality observed on every interaction.

Autonomous resolution

End-to-end resolution on real conversations, deflections become resolutions, so your team handles only what truly needs them.

Lower cost to serve

TokenTrim™ cuts tokens ~10× per interaction, so you scale to 24/7 coverage without scaling compute spend.

Continuous evaluation

autoQA scores 100% of interactions, turning compliance and quality into a live, learnable signal that powers RSI.

Real-time latency

Sub-500ms at p95 with natural turn-taking and barge-in, fast enough that callers never feel they're waiting on a machine.

Neural voices

800+ voices across 155+ locales with brand-voice cloning and live speech-to-speech translation that preserves tone.

Connectors

It works where you already work, CRM, billing, telephony, data warehouse, wired over MCP with a headless, API-first core.

Built by the team that pioneered LLM-for-CX

We didn’t pivot into AI. We started here.

auto-AGENTS™ is built by Commerce.AI, the team that turned customer conversations into product and market intelligence, then brought large language models to the contact center before "agentic" was a word. Every interaction is signal: it resolves the call, and it teaches the system. That feedback loop, conversations to intelligence to autonomous action, is exactly what RSI compounds.

Inside the engine →

Commerce.AI · origin

Conversations → intelligence

Mined customer conversations into product & market intelligence, learning, at scale, what people actually want.

First wave

LLMs in the contact center

Among the first to put frontier LLMs on live calls, reasoning and grounded answers where decision trees used to deflect.

Now

Agentic + autonomous

Agents that act over MCP, evaluate themselves with autoQA, and orchestrate background sub-agents end-to-end.

Today · live

RSI in production

A closed loop that retunes prompts, routing and TokenTrim™ every week, quality up, cost down, every change gated.

Connected to your stack

170+ connectors. Autonomy where you already work.

FAQ

Why auto-AGENTS™, answered.

Scripted bots match utterances to a fixed menu and deflect anything off-script. auto-AGENTS™ reason on frontier LLMs over intent, account state and policy, then take real action over MCP, refunds, lookups, scheduling, and learn from the outcome. The result is autonomous resolution, not deflection.

A closed production loop: resolve → measure → learn → optimize. autoQA scores 100% of interactions, winning policies are promoted, and prompts, routing and TokenTrim™ inference-compression retune themselves every week. Quality compounds and cost per resolution decays, with every change gated by adversarial evals plus human approval, and fully reversible.

The model is the easy part. The moat is the runtime around it, agentic action over 170+ MCP connectors, the autoQA eval system on 100% of calls, TokenTrim™ economics, multi-agent orchestration, enterprise security, and the RSI loop that makes it better in your environment over time. That compounding system is what you can't get from an API key.

Self-improvement is governed end-to-end. Each candidate change must clear the generated adversarial eval suite and human approval before promotion, and everything runs against your policies with field-level encryption, PII redaction, an immutable audit trail and instant rollback. The agent never silently ships changes to itself.

It's built by Commerce.AI, the team that pioneered turning customer conversations into product and market intelligence, then brought LLMs to the contact center early. That conversations-to-intelligence DNA is exactly what RSI runs on: every interaction both resolves a customer and teaches the system.

See it live

Describe your hardest call. We'll ship the agent.

Bring a real scenario. In 30 minutes we'll build it, evaluate it, and resolve it end to end, on your systems, in your languages, improving from the first interaction.

Book a demo Explore the engine

Intelligence per dollar, the only metric that scales.

What it costs at your volume

Cheaper per contact. Warmer per customer.

Scripted bots deflect. auto-AGENTS™ resolve.

The loop that drives cost per resolution down, every week.

~10× cheaper inference

100% evaluated

Gated & reversible

Every other class makes you choose. auto-AGENTS™ doesn’t.

What you only get from auto-AGENTS™.

It improves itself in production

Graded on 100% of interactions

Frontier quality at production cost

Built from conversations-to-intelligence

Bought a bot. Got a queue. Then found a system.

Reasoning, action, and evaluation, wired into one runtime.

It understands the ask, not just the keyword.

It doesn't just talk. It acts on your systems.

It grades itself before, and after, it ships.

The business case writes itself.

Autonomous resolution

Lower cost to serve

Continuous evaluation

Real-time latency

Neural voices

Connectors

We didn’t pivot into AI. We started here.

170+ connectors. Autonomy where you already work.

Why auto-AGENTS™, answered.

Describe your hardest call. We'll ship the agent.

Outcomes teams feel in weeks.