> 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/guides.md).

# Guides

- [Make your first query](https://docs.algenta.ai/guides/first-query.md): Run your first governed query against a registered dataset and read the deterministic result, using HTTP, the de CLI, or the Python SDK.
- [Group data locally with Query.group\_by()](https://docs.algenta.ai/guides/local-query-groupby.md): Build a columnar query with algenta.Query and group your own data locally with group\_by() — the same composed engine the dataframe group-by benchmark measures.
- [Run a decision](https://docs.algenta.ai/guides/run-a-decision.md): Turn a scenario into a recommended action — call simulate or recommend and read the decision envelope (recommended action, expected value, confidence, percentiles).
- [Drive a governed decision](https://docs.algenta.ai/guides/governed-decisions.md): Frame a decision case, run the governed analysis, inspect the evidence, then propose, approve, and execute an action plan through the host-independent Decision API
- [Renewal Capacity Allocation pack](https://docs.algenta.ai/guides/renewal-capacity-allocation.md): Allocate limited customer-success hours across upcoming renewals to maximize model-estimated retained ARR, modeling intervention effects explicitly under hard constraints.
- [Simulate a scenario](https://docs.algenta.ai/guides/simulate.md): Build a full simulation request — auto vs expert mode, choose a simulation\_model, declare distributions and params, pin a seed, and read the percentiles back.
- [Kernels on Hugging Face](https://docs.algenta.ai/guides/kernels-on-hugging-face.md): Load Algenta compute kernels on Apple silicon straight from the Hugging Face Kernel Hub with the kernels library.
- [Run a what-if sweep](https://docs.algenta.ai/guides/what-if.md): Sweep one simulation variable across a range to see how outcomes move, and stream a long simulation live over Server-Sent Events.
- [Recommend, score & compare](https://docs.algenta.ai/guides/recommend-and-score.md): Compare named actions and get a ranked recommendation, score a single scenario with custom weights, run many simulations at once, and compare scenarios side by side.
- [Query governed data](https://docs.algenta.ai/guides/governed-query.md): Run a deterministic governed query end to end — resolve structured intent into an exact plan, execute it with typed filters, and read plan\_hash and validated.
- [Log decisions & record outcomes](https://docs.algenta.ai/guides/decision-memory.md): Log a decision through the engine, record its real-world outcome later, and review the closed loop so the engine learns which decision types are over or under confident.
- [Drive decisions through the API](https://docs.algenta.ai/guides/decision-memory-api.md): Drive the full Algenta decisions API — plan, log, execute, and delete decisions — and call the one-line product endpoints for decision, agent, optimize, retrieve, and forecast.
- [Configure the execution policy](https://docs.algenta.ai/guides/execution-policy.md): Read and update the Algenta execution policy — the confidence, risk, calibration, and idempotency gates that guard autonomous decision execution — and inspect calibration.
- [Connect a data source](https://docs.algenta.ai/guides/connect-data.md): Register an external source as a saved connector, test it, browse its schema, and onboard a dataset so the engine can query it under a typed contract.
- [Map a dataset instantly](https://docs.algenta.ai/guides/instant-map.md): Drop in CSV, JSON, or connected tables and get back a unified schema with every field mapped, join keys detected, and confidence for each match — no schema, no ETL.
- [Route & execute capabilities](https://docs.algenta.ai/guides/capabilities.md): Route an objective to the right capability and execute it through the capability plane, using HTTP, the de CLI, or the Python SDK, with execution ownership made explicit.
- [Register capability providers & bindings](https://docs.algenta.ai/guides/capability-bindings.md): Register capability providers, create and test bindings, discover and authorize capabilities, and enable skills — the setup layer behind the Algenta capability plane.
- [Connect an external MCP server](https://docs.algenta.ai/guides/external-mcp.md): Register an external MCP server with Algenta — discover its tools into the unified capability catalog and let the engine call them on your agent's behalf, all under the egress policy.
- [Run an agent](https://docs.algenta.ai/guides/agent-runs.md): Start a deterministic, replayable agent run through the Algenta Agent Runtime, follow its lifecycle, stream its events, and retrieve its checkpoints and proof artifacts.
- [Run an async job](https://docs.algenta.ai/guides/async-jobs.md): Submit a long-running simulation as an async job, poll its status by id, fetch the result when it completes, and cancel a queued or running job.
- [Triage a repository](https://docs.algenta.ai/guides/repository-intelligence.md): Triage a code repository with the Algenta engine — snapshot it, reduce it to a bounded evidence bundle, and read back the findings over the HTTP API
- [Fix a repository](https://docs.algenta.ai/guides/repository-fix.md): Turn a triage evidence bundle into a verified code fix — generate a decision plan, simulate its risk gate, and apply it as a patch, a local branch, or a pull request.
- [Prove a security finding](https://docs.algenta.ai/guides/security-analysis.md): Prove reachability for scanner findings against a repository snapshot, triage by classification, and re-prove that a fix closes the finding after you patch it.
- [Register a trigger](https://docs.algenta.ai/guides/triggers.md): Register a threshold condition on a data source, fire it to run an attached simulation, pause or resume it, and delete it — with optional webhook and auto-execution dispatch.
- [Webhooks](https://docs.algenta.ai/guides/webhooks.md): Receive Algenta events by webhook — async job callbacks, trigger notifications, and decision-execution dispatch — verify delivery with a test POST, and receive inbound GitHub App events.
- [Bring your own key (BYOK)](https://docs.algenta.ai/guides/byok.md): Supply your own LLM-provider or engine credential. Algenta stores it encrypted at rest, never returns the key material, and uses it only to serve your own org's calls.
- [Device binding](https://docs.algenta.ai/guides/device-binding.md): Bind an Algenta offline license to a machine identity so it cannot be copied, print the device id, verify a bound license, and fix a device\_identity\_mismatch.
- [Run air-gapped](https://docs.algenta.ai/guides/air-gapped.md): Run Algenta fully air-gapped with no outbound network — install and verify an offline RS256 license, prove the egress policy fails closed, and set the engine for no-LLM or BYOK operation.
- [Use Algenta in CI/CD](https://docs.algenta.ai/guides/ci-cd.md): Run Algenta decisions, simulations, and repository triage from CI/CD with the de CLI — authenticate from a secret, emit JSON, and gate the pipeline on exit codes.
- [Run local LLM decode](https://docs.algenta.ai/guides/run-qwen3-decode.md): Install Algenta, get a supported checkpoint, and run greedy decode on your own Apple Silicon GPU — requirements, limitations, and a complete working example. Currently tested on Qwen3 models.
- [Model-agnostic LLM inference](https://docs.algenta.ai/guides/run-qwen3-family-inference.md): Run any supported checkpoint on Apple Silicon through Algenta's model-agnostic SDK path — supported shapes, long context, and the quantized load cache. Currently verified on Qwen3 models.


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