Finance and FP&A
Value, run cost and return per dollar for every agent, with a weekly PDF and CSV that shows each assumption.
2.0 Workload routing, shared workflow credit and verifiable records →
A2EA
Agent-to-Economic Attribution · Patents pending
Prove the ROI of every AI agent you run.
A2EA measures what each agent's work is worth in dollars, against your human process and your other agents. Finance gets a number it can defend, operations knows which agents to scale, and every score comes with a record anyone can check.
- First 100 agent tasks free
- Task metadata only, never your prompts or customer data
- Works with Python, TypeScript, LangChain, CrewAI and OpenAI Agents
Connects to the agents you already run
PythonTypeScriptNode.jsLangChainCrewAIOpenAI Agents SDKSlackWebhooks
The problem
AI agent spend is growing. The proof of return is not.
Most companies can say how many agents they run and what they cost. Very few can say what that work was worth, which agents are earning their keep, or show evidence that holds up when finance, the board or an auditor asks.
"What are we getting for this?"
Usage dashboards show activity and cost. They don't show value against the way the work was done before.
"Which agent should get more work?"
Without a common measure, teams scale the agent with the best demo, not the one with the best results.
"Can you prove that number?"
Spreadsheet ROI estimates don't survive an audit. A2EA keeps the inputs behind every score so it can be recomputed.
Who it's for
One measure of agent value that every team can use.
A2EA gives each team that owns part of your AI program the answer it needs, from the same scored records.
Operations leaders
Clear "use more" and "use less" calls per agent, and work shifted toward the agents that earn it, within your limits.
AI and platform teams
One wrapper or framework connector per agent. Results are signed locally and retried safely, never counted twice.
Risk, audit and compliance
Every score sealed in a hash chain with an independent copy, roles enforced on the server, and a full recompute on demand.
Live demo · No account needed
See a real score in about 10 seconds.
Pick an example workload and run it. A2EA scores a batch of task results on our server and shows the dollar value, where it came from, and the record hash.
1 · Pick a workload
2 · Run it
Waiting for you to press Run free score.
Demo tasks are simulated and unsigned, so their value counts at half weight. Nothing is saved.
Measured value
$—
Tasks scored—
Success rate—
Avg. time per task—
Where the value came from
Record hash appears after the run
How it works
From first agent to board-ready report.
01
Connect
Add one wrapper or a LangChain, CrewAI or OpenAI Agents connector. Each task's type, duration and outcome is signed with your workspace key.
02
Measure
Accuracy, speed and after-hours coverage are compared with your human process, your other agents and the agent's own history, then valued in dollars.
03
Decide
Once an agent has enough evidence, A2EA recommends using it more or less, with a plain reason and a confidence level.
04
Act and report
Shift each agent's share of work automatically or after approval, roll back any change, and send finance the weekly report.
The platform · What's in 2.0
Manage your agents like an investment portfolio.
Measure the return, put more work behind what performs, and keep the evidence.
MeasureKnow what each agent returns
- Value leaderboard and ROI. Value, run cost and return per dollar for every agent.
- Three baselines. Your human process, other agents on the same work, and the agent's own history.
- Shared workflow credit. When several agents work one job, value is split to the cent and never counted twice.
DecidePut work where it pays
- Workload routing. Shares of work move toward the agents that earn them, with limits, a cooldown and rollback.
- Agent maturity. New agents start from a named benchmark and earn confidence as evidence builds.
- Alerts. Slack and webhook alerts when an agent's error rate rises or its value drops.
ProveKeep evidence that holds up
- Verifiable score records. Inputs kept with every score, sealed in a hash chain with an independent copy.
- Weekly PDF and CSV. Week over week, every recommendation and routing change, with a matching fingerprint.
- Workflow lineage. How steps connected and which links are uncertain, instead of a guess.
What A2EA reports
Numbers your CFO can defend.
Every figure is tied to signed task results and the assumptions you set. A2EA is just as clear about what it does not claim.
How scores are calculated →- Work doneWhat each agent ran, how long it took and whether it succeeded, from signed results.
- Value createdDollar value against your baselines, with every assumption shown.
- Time savedHours saved against your baseline, valued at your labor rate.
- HeadcountNot measured. Time saved is workload, not jobs, and A2EA never reports it as job cuts.
Security and governance
Built to pass a security review.
Here is exactly what A2EA does with your data, and the controls behind it.
Task metadata only
A2EA receives task type, timing and outcome. Not your prompts, customer data or agent output.
Workspace isolation
Each customer workspace has its own data store. One customer can never read another's agents.
Roles on the server
Owners, editors and viewers, checked on every request. Only owners hold API keys and billing.
Signed task results
Every result is signed with HMAC-SHA256. Changing a result, its workflow or its parent breaks the signature.
Tamper-evident records
Scores are hash-chained, with an independent copy kept outside your workspace database.
Safe outbound alerts
Alert webhooks are signed, and private or internal network addresses are refused.
For your engineers
Live in an afternoon, not a quarter.
Wrap an agent function and A2EA times, signs and scores every task. Retries keep their ID, so nothing is credited twice.
- Python
pip install a2ea-py - TypeScript
npm install a2ea-sdk
import os from a2ea import A2EAClient client = A2EAClient(api_key=os.environ["A2EA_API_KEY"]) @client.track_agent(task_type="invoice_review") def review(invoice): return agent.run(invoice) # timed, signed, scored
import { A2EAClient } from 'a2ea-sdk'; const a2ea = new A2EAClient({ apiKey: process.env.A2EA_API_KEY }); const review = a2ea.wrap(checkInvoice, { taskType: 'invoice_review' }); await review(invoice); // success or failure, recorded once
from a2ea.integrations.langchain import A2EACallbackHandler handler = A2EACallbackHandler(task_type="invoice_review") chain.invoke(invoice, config={"callbacks": [handler]}) # CrewAI: instrument(...) · OpenAI Agents: install(...)
Pricing
Start with one agent. Scale when the numbers prove it.
Free test
$0
100 scored agent tasks, a managed workspace, API key and the full dashboard.
Start free
Usage
Pay as you go
Keep scoring after the free tasks. Billing shows usage alongside the value measured.
See pricing
Enterprise
Custom
Procurement and security review, rollout planning, and custom data and governance needs.
Talk to usWhat data does A2EA see?
Task metadata: the task type, how long it took, whether it succeeded, and optional workflow links. A2EA does not receive your prompts, customer data or agent output.
How is an agent's dollar value calculated?
Accuracy, speed and after-hours coverage are compared with your baselines, weighted for the type of work, and converted to dollars using the labor rate and error cost you set. Every assumption is shown in the score and the weekly report.
How long does setup take?
Most teams connect a first agent with one wrapper or framework connector, and see a first score as soon as tasks start flowing.
Can A2EA change which agent does the work?
Only if you turn routing on. You set the limits and cooldown, choose automatic or approve-first, and can roll back any change.
Can our auditors check a score?
Yes. Each score keeps its inputs and is sealed in a hash chain with an independent copy, so it can be recomputed and checked for tampering.
Does A2EA report jobs replaced by AI?
No. Time saved measures workload, not headcount, and A2EA never presents it as jobs lost.
The AI Modularity stack
Before, during and after every agent action.
Before deploy
Agent Verify
Find leaked keys, risky tools and prompt-injection holes before an agent ships.
Agent Verify →At execution
A2SPA
Authorize each consequential action with a signature, so nothing runs without one.
A2SPA →After the work
A2EA
Measure what the work was worth, prove it, and route more of it to the agents that earn it.
Try the demo →No Signature. No Execution.™
Find out what your agents are worth.
Connect one agent and see its value, its run cost and a recommendation. The first 100 tasks are free, or book a walkthrough with our team.