Can it recommend a new price from a list-price gap?

No. A gap between list and realized price has many possible explanations. The agent first checks definitions, segments, discount reasons, contract terms, costs, and outcomes, then proposes validation before any price decision.

Does the agent calculate price elasticity?

Only when suitable price, demand, segment, and experiment data support a defensible estimate. It will state assumptions and uncertainty and will not invent an elasticity value from a single aggregate snapshot.

Will it change prices or run experiments automatically?

No. It can prepare an experiment plan and guardrails. Price changes and customer-facing tests require the user's explicit decision and authorized systems; access is never assumed.

Autonomous Pricing Intelligence

Example session with this skill installed

Using this fictional SaaS data only, diagnose pricing opportunities: list price $100/month; average realized price $82; 40% of accounts receive discounts; gross margin is 70%; three tiers exist but no feature usage or segment data is available. Suggest evidence needed and 3 experiments. Do not infer elasticity or recommend a price increase as established fact.

Read your context and instructions
Compiled the autonomous pricing intelligence

Pricing opportunity review — fictional SaaS

Executive summary

The data show a $18 gap between list and average realized price and that 40% of accounts receive discounts. These facts warrant a discount and packaging review, but they do not prove discount leakage, willingness to pay, or an optimal price. With 70% gross margin, any change should be tested against contribution, conversion, retention, and customer value.

Baseline and evidence limits

Measure	Provided value	What it supports	What remains unknown
List price	$100/month	Published reference price	Price by tier, segment, channel, and date
Average realized price	$82/month	Aggregate realization is $18 below list	Distribution, contract term, currency, and mix
Accounts discounted	40%	Discounting is common in the supplied snapshot	Discount size, rationale, approval, renewal, and cohort
Gross margin	70%	A baseline margin figure was supplied	Cost allocation, usage variability, and segment margin

Hypotheses to test

Discount governance: Some discounts may be inconsistent or unnecessary. Audit a representative set of deals and renewal outcomes before changing policy.
2.

Package fit: Tier boundaries may not reflect customer value or usage. Map features and usage by segment, then interview buyers and non-buyers.
3.

Value communication: Buyers may not understand differentiated outcomes. Test clearer package descriptions before changing price points.

Experiments

Deal review: Sample recent new and renewal contracts; classify discount reason, approval, term, segment, usage, and outcome. Protect customer confidentiality.

Packaging research: Conduct structured interviews across current customers, lost deals, and prospects; validate stated priorities against behavior and product usage.

Controlled offer test: If legally and operationally appropriate, test one clearly defined package or messaging change with randomized eligible prospects. Predefine conversion, retention, margin, fairness, and stop guardrails.

Decision gates

Do not set a new price from this snapshot alone. Confirm data definitions and cohorts, calculate segment-level contribution, understand contractual and regulatory constraints, and estimate uncertainty. Stop or roll back a test if customer harm, complaint rates, or retention risks exceed the agreed threshold. Record results and update the pricing decision log.

Conclusion: The visible opportunity is to learn why realized prices differ from list and whether packages match customer value. No price elasticity or financial lift is established.

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About this skill

What this skill does

Autonomous Pricing Intelligence evaluates pricing architecture, realized prices, customer value, willingness-to-pay evidence, packaging, discounts, costs, margins, segments, competitors, and strategic positioning. It identifies testable pricing opportunities without defaulting to arbitrary increases or copying competitors.

Workflow

Clarify objectives and constraints; map price and package structure; establish an evidence-backed baseline; assess segment value and price realization; examine alternatives, costs, discounts, and channel economics; identify hypotheses; design safe experiments; define guardrails and success measures; record results and next actions.

Deliverables

Pricing baseline, package map, realized-price analysis, segment hypotheses, evidence gaps, opportunity ranking, experiment briefs, guardrails, decision log, and a reusable price watchlist.

Limits

It never invents prices, willingness to pay, elasticity, margins, conversion, churn, competitor offers, costs, or financial outcomes. Live data, experiments, monitoring, and alerts require host support and authorization.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Autonomous Pricing Intelligence