agent optimization layer

Frontier Agents.
2× More Efficient.

Induction automatically optimizes agent workloads to extend context windows and reduce costs. Same models and success rates. Zero code changes.

See the benchmarks
01 — The Challenge

Agent efficiency is not a given.

Without active efficiency management, your most capable agent can also become your most expensive.

As agents work, their context fills with information that is no longer relevant but continues to be sent to the model on every step, consuming context space and inference budget. Caching and right-sized models can reduce the cost, but they do not eliminate the underlying waste. As your agents take on more complex tasks and longer workflows, that waste compounds.

Task length 2x → cost 4x.
  • Baseline
  • + Induction
02 — How it works

Make every token count.

Induction continually monitors and learns your workloads, classifies relevant versus stale context, and automatically removes redundancies in real time. The result is focused context that keeps wasted tokens low and success rates high.

Raw context16 units
induction
Optimized9 units
03 — The numbers

Proven on agent benchmarks.

Across demanding agent workloads, Induction completes the same tasks for 37–67% less. Pass rates hold at baseline in every configuration tested.

All-in cost per benchmark run — gpt-5.6-sol
  • + Induction
  • Baseline
Reasoning
Pass rate
tau3-banking
med
−37%
$37$59
31.3%28.6%
xhigh
−60%
$52$130
39.7%40.0%
max
−67%
$74$225
47.1%43.8%
toolathlon
med
−40%
$40$67
67.8%67.5%
xhigh
−58%
$67$160
71.0%72.0%
max
−55%
$105$234
76.4%75.9%
Pass-rate differences are within measurement noise on all six configurations.
04 — Where the gains go

How will you use 2× more efficiency?

Induction gives your agents more capacity within the resources you already have. How you put those gains to work is up to you.

  • Do much more

    Run more tasks. Serve more users. Automate work that previously didn’t make economic sense. The same infrastructure and inference spend can support substantially more agent activity.

  • Use more intelligence

    Tackle more challenging workloads and improve success rates. Shift the savings towards higher reasoning and stronger models. Keep the cost constant.

  • Improve unit economics

    Simply keep the efficiency. Maintain the same success rates while reducing the cost of delivering them. Lower inference spend, better margins, more room to scale.

Task pass rate vs. all-in inference cost — gpt-5.6-sol
  • + Induction
  • Baseline

Each pair is one model and reasoning configuration: baseline and the same workload with Induction; bars show 95% confidence intervals. Induction shifts each configuration towards the lower cost quadrant.

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