Multi-provider agentic AI

Automate software development with AI agents you can trust

Mynth turns an engineering task into working output — orchestrating AI agents across four LLM providers with routing, fallback, and evaluation built in. Thousands of agentic runs every day at 99.99% reliability, and up to 50% lower cost per run.

  • Model-agnostic across 4 providers
  • 99.99% reliability
  • Now onboarding new teams
mynth · live orchestration routed
Engineering intent Mynth orchestration prompt · context · tools · eval Provider A Provider B selected Provider C Provider D Working output
Per-run cost ▼ ~45%
99.99% uptime
99.99%
Reliability uptime
Thousands
Agentic runs / day
4
LLM providers routed
~40–50%
Lower per-run cost
The problem

Building software is your biggest cost line — and the hardest thing to automate reliably

Global spend on software is in the trillions — salaries, tooling, contractors. AI can now do real engineering work, but three forces decide who profits from it.

Inference cost eats your margin

Model tokens are the primary cost of agentic software. Without control, every run is expensive.

One model, one point of failure

A single-vendor dependency means one outage or price hike can halt everything.

Unreliable agents break trust

Agents that hallucinate or stall can’t be trusted with real engineering work.

How it works

From a goal to shipped work, automatically

The full agentic loop — intent becomes context, context drives tools, tools produce output, and every step is evaluated.

01

Describe the intent

Express an engineering goal in plain language. Mynth converts that intent into a structured, executable set of tasks.

02

Assemble context & orchestrate

The agent core plans the work, calls the right tools, and assembles the precise context each step requires.

03

Route to the right model

For every step, Mynth picks the cheapest capable model across four providers — with fallback and caching handled automatically.

04

Evaluate & deliver

Guardrails and in-loop evaluation stop low-quality runs early. The result is working output, backed by full telemetry.

The cost-control moat

The team that controls inference cost wins on margin

In this category, model tokens are the primary cost of goods sold. Mynth is engineered around the levers that move them.

Provider routing & arbitrage

Across four providers, Mynth selects the cheapest model capable of each step — every single run.

Built-in fallback

When a provider degrades or fails, work moves seamlessly — so you never re-bill a failed run.

In-loop evaluation & guardrails

Quality checks inside the loop stop bad output early, saving tokens before cost compounds.

The result

A combined ~40–50% reduction in per-run inference cost — the dominant cost of agentic software.

Per-run inference cost vs. single-provider
Without Mynth
100%
With Mynth
~55%
40–50%
lower cost per run through routing, fallback & evaluation
Why Mynth

A platform, not a thin wrapper

The durable value sits in the routing, evaluation, and observability layers — not in a single prompt. That’s where Mynth is built to compound.

Model-agnostic

Routing across four providers, not locked to a single vendor.

Reliability-grade

99.99% uptime target with full telemetry across every run.

Cost-engineered

Routing, fallback, and evaluation cut inference cost ~40–50%.

Full agentic loop

Intent → context → tools → output, captured end to end.

Built like infrastructure

20+ services on Kubernetes, owned end to end by one team.

Observable by design

Quality, latency, reliability, and cost in a single view.

Now accepting new teams

Ready to automate your engineering workflow?

See how Mynth turns engineering intent into reliable, cost-controlled output. Book a personalized demo with our team — we’re onboarding new teams now.