Model the economics of autonomous agents, net of inference cost.
Built for AI, digital and operations leaders scoping their first production agent program. Autonomy rate and run cost are the levers that decide whether the business case holds.
Your inputs
Benchmarks show typical enterprise ranges — override every field with your own numbers.
Multi-step knowledge-work tasks an agent could own end-to-end
Benchmark: 20k–150k for a single process family
Human end-to-end time today, including tool switching
Benchmark: 8–25 minutes for mid-office knowledge work
Share of tasks completed end-to-end with no human escalation
Benchmark: 35–60% in year one on a scoped process
Human still finishes, but the agent has done the retrieval and drafting
Benchmark: 15–30%
Benchmark: $80k–$120k in US enterprise operations
Includes retries, evaluation calls and tool invocations
Benchmark: $0.15–$0.80 per multi-step run
Agent design, tool integration, evaluation harness, guardrails, change management
Benchmark: $300k–$800k for a production agent program
Labor value returned minus inference and orchestration cost, at your autonomy rate.
Directional estimate. Assumes 1,560 productive hours per worker per year and a fully-loaded cost per successful agent run including retries.
Three-scenario view
Finance reviewers expect a range. These scenarios flex adoption and implementation cost around the model you entered.
Slower adoption, higher integration effort
Your inputs as entered
Strong sponsorship, clean data, phased scale-up
Want a quote built on these numbers?
Send us the brief and a delivery lead validates these assumptions against your data, then replies with indicative scope, timeline and commercial options.
CalculatorModel the economics of autonomous agents, net of inference cost. — routed to this team
How enterprise leaders use this model
- What makes agentic ROI different from automation ROI?
- Agentic systems act across multiple steps and tools, so value is driven by the share of tasks completed end-to-end without escalation — not by per-transaction time savings alone. Autonomy rate is the single biggest swing factor.
- What autonomy rate should we assume in year one?
- Most enterprise programs land between 35% and 60% fully autonomous completion in the first year on a scoped process family, rising as guardrails, evaluations and tool coverage mature.
- Should we include inference cost?
- Yes. Agentic runs consume far more tokens than single-shot copilots. The model here charges a fully-loaded cost per successful run, including retries and orchestration overhead.
- How do we defend this model to finance?
- Run the conservative scenario first, hold the aggressive case for the second phase, and share the URL — your inputs are encoded in the link so reviewers see the identical model.
How do you calculate the ROI of agentic AI?
Agentic AI ROI is the value of tasks the agent completes autonomously, minus inference and orchestration run cost, minus the cost of escalations the agent creates or fails to resolve, minus the annual build and governance investment. Autonomy rate and run cost per task are the two variables that decide whether the programme is net positive.
Ungated — results appear instantly, no email required.
What you enter
- Monthly task or workflow volume
- Fully loaded cost of a human completion
- Autonomy rate — share completed end to end without a human (%)
- Escalation rate and cost of an escalated task
- Inference and orchestration run cost per task
- Annual build, integration and governance investment
How it is calculated
- 1.Annualize task volume and split it into autonomous completions, escalations and failures.
- 2.Value autonomous completions at the fully loaded human cost they replace.
- 3.Charge every task — including escalated ones — the inference and orchestration run cost.
- 4.Add the handling cost of escalations back as a deduction.
- 5.Subtract the annual investment to get net value and payback.
What you get back
- Net annual value after run cost
- Effective cost per autonomous completion
- Break-even autonomy rate
- Scenario range and PDF export
Built for: CIOs, Chief AI Officers and enterprise AI leaders moving agents from pilot to portfolio.
Why do agentic AI business cases fail after the pilot?
Because pilots measure task success and production measures unit economics. Run cost per task, escalation handling and the governance overhead only appear at volume, and together they routinely turn a positive pilot into a negative portfolio line.
What autonomy rate does an agentic AI deployment need to break even?
It depends entirely on the cost of the human completion being replaced versus run cost per task. The calculator solves for that break-even directly from your own numbers rather than assuming a benchmark.