> For the complete documentation index, see [llms.txt](https://docs.algenta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.algenta.ai/sdks/python/decisions-and-products.md).

# Decisions & products

These methods record decisions in [decision memory](/concepts/decision-memory.md) and drive product-level intelligence (decision, optimize, retrieve, forecast). They are available on both `AlgentaClient` and `AsyncAlgentaClient` (await the async calls). The product methods take a request dict; the decision-memory methods take a decision id plus typed keyword arguments.

## Method reference

| Method                                                                                                                   | Purpose                                                                                                                                    |
| ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `product_decision(request)`                                                                                              | Make a product-level decision (`ProductDecisionResult`). Posts to `/v1/decision`.                                                          |
| `product_agent_run(request)`                                                                                             | Run a product agent workflow (`ProductAgentRunResult`). Posts to `/v1/agent/run`.                                                          |
| `product_optimize(request)`                                                                                              | Optimize a product objective (`ProductOptimizeResult`). Posts to `/v1/optimize`.                                                           |
| `product_retrieve(request)`                                                                                              | Retrieve ranked product results for a query (`ProductRetrieveResult`). Posts to `/v1/retrieve`.                                            |
| `product_forecast(request)`                                                                                              | Forecast product metrics over future periods (`ProductForecastResult`). Posts to `/v1/forecast`.                                           |
| `log_decision(request)`                                                                                                  | Record a decision in decision memory (`DecisionLogResult`). Posts to `/v1/decisions`.                                                      |
| `list_decisions(*, page=None, limit=None, page_size=None, with_outcome_only=None)`                                       | List recorded decisions (`DecisionListResult`). Reads `/v1/decisions`.                                                                     |
| `get_decision(decision_id)`                                                                                              | Fetch a single recorded decision (`DecisionLogResult`). Reads `/v1/decisions/{decision_id}`.                                               |
| `record_outcome(decision_id, *, actual_outcome, outcome_notes=None)`                                                     | Attach the realized outcome to a decision (`DecisionLogResult`). Posts to `/v1/decisions/{decision_id}/outcome`.                           |
| `execute_decision(decision_id, *, webhook_url, timeout_seconds=10.0, force=False, override_safety=False, metadata=None)` | Execute a recorded decision via a webhook and return a receipt (`ExecutionReceiptResult`). Posts to `/v1/decisions/{decision_id}/execute`. |
| `delete_decision(decision_id)`                                                                                           | Delete a recorded decision. Sends `DELETE /v1/decisions/{decision_id}`.                                                                    |

## Example: log a decision and record its outcome

```python
import os
from decision_engine import AlgentaClient

client = AlgentaClient(api_key=os.environ["ALGENTA_API_KEY"])

# Record a decision.
logged = client.log_decision(
    {
        "chosen_action": "Scale checkout service",
        "expected_value": 42000,
        "context": "Checkout p95 latency at 820ms",
    }
)

# Later, attach what actually happened.
updated = client.record_outcome(
    logged.id,
    actual_outcome=39500,
    outcome_notes="Latency improved; revenue slightly below forecast.",
)

# Review decisions that already have an outcome.
history = client.list_decisions(with_outcome_only=True, limit=20)
for decision in history.decisions:
    print(decision.id, decision.chosen_action)
```

## Example: forecast product metrics

```python
import os
from decision_engine import AlgentaClient

client = AlgentaClient(api_key=os.environ["ALGENTA_API_KEY"])

forecast = client.product_forecast(
    {
        "product_id": "sku-1042",
        "metric": "units_sold",
        "horizon": 6,
    }
)
for period in forecast.periods:
    print(period.period, period.forecast)
```

## Related pages

{% content-ref url="/pages/tGlnNUppZSD65MAWJvD0" %}
[Log decisions & record outcomes](/guides/decision-memory.md)
{% endcontent-ref %}

{% content-ref url="/pages/ktXIojcv61EsP0IhlJTb" %}
[Decision memory](/concepts/decision-memory.md)
{% endcontent-ref %}

{% content-ref url="/pages/l0mqGrhl5ic9OfaGy52Q" %}
[Simulations & queries](/sdks/python/simulations-and-queries.md)
{% endcontent-ref %}


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