AI-Driven Optimization
AI-driven optimization is Athia's execution layer: agents that decide what happens to a transaction rather than reporting on it afterwards. It is the one pillar that needs Athia in the transaction path, or, for merchant-initiated recurring billing, the batch file exchange. See DEUNA Orchestration for how that works.
Agents#
Athia's optimization is delivered by agents that act inside the payment flow, not by reports produced after it. An agent decides transaction by transaction, in real time, and improves from every outcome rather than from rules someone maintains.
Each outcome, approved, declined, retried, disputed, feeds back into the agent, and its decisions sharpen as your volume accumulates.
Agents fail open: if an agent does not answer in time, the transaction proceeds on your static rule configuration, so an agent never blocks a payment. See DEUNA Orchestration for the runtime behavior.
Specialized agent portfolio
| Agent | Bounded objective | Detailed guide |
|---|---|---|
| Conversion Optimization | Reduce avoidable checkout and payment-presentment friction. | Conversion Optimization Agent |
| Acceptance Optimization | Improve authorization outcomes through supported routing, messaging, and retries. | Acceptance Agent |
| Cost Optimization | Reduce processing cost within acceptance, risk, and commercial constraints. | Cost Optimization Agent |
| Reconciliation | Match financial evidence and prioritize unresolved exceptions. | Reconciliation Agent |
| Disputes | Consolidate case evidence, deadlines, and governed provider actions. | Disputes Agent |
The exact authority of every agent depends on connected data, provider capabilities, permissions, and account configuration. See Agentic Optimization for the common policy and rollout model.
Acceptance Agent#
The Acceptance Agent maximizes authorization success by routing each transaction to the processor most likely to approve it, correcting the payment message before it reaches the processor, and retrying only the declines worth retrying. It weighs the context of each transaction—card type, issuing bank, amount, customer history and time—and keeps those decisions current on its own.
See Acceptance Agent for what it does and the two ways to deploy it.
Inside the payment flow the agent is consulted within the DEUNA orchestration decision, on the same call rather than as a separate hop. See DEUNA Orchestration for how it behaves at runtime.
The batch file exchange#
Where DEUNA is not in the payment path, merchant-initiated recurring billing is still an execution surface, because a scheduled charge can be decided before it runs.
You deliver a file of the charges due, the agent scores every row against your own history, and a result file comes back with a recommended processor, message configuration, retry guidance and an expected approval rate per row. You apply it with your own processor connections, or DEUNA runs the payments for you. Nothing about how you take payments changes, and there is no integration.
The proposed input and output columns are on Acceptance Agent. Treat them as a starting point rather than a specification: your Athia team agrees the final shape with you and supports the integration throughout.
The one obligation that comes with it: the status of every scored transaction has to come back, or the agent stops learning from the volume it is deciding.
Experimentation#
No agent is switched on and left to run. Every agent moves up the same ladder before it carries real volume, and DEUNA runs each stage with you. you are approving evidence at each step, not operating a testing framework.
The quantity that escalates is the share of your traffic the agent decides, and it starts at zero. You approve at each gate against a readout, and the weights move back as easily as they move forward. Shadow mode stays available at any point, so nothing here is one-way.
| Stage | What happens |
|---|---|
| Shadow mode | The agent scores your live traffic and records the decision it would have made, without acting on any transaction. This is where its judgment is checked against what actually happened on your own volume, before anything is at stake. |
| Controlled variants | A share of traffic follows the agent while the rest stays on your current configuration, so the two are compared like for like. The weights are agreed with you and moved gradually as results hold. |
| Measured lift | Matched cohorts, acceptance-rate benchmarks, statistical significance and a financial readout, so the decision to give the agent more volume rests on evidence rather than judgment. |