Maxim AI

No code agents call third party services through API nodes at any point in a chain and deploy without code changes, and the documentation walks through an agent that categorizes support emails, creates help desk tickets and sends replies. Prompts can use tool calls and connected MCP servers in the playground, and alerts go to Slack and PagerDuty.

SourceMaxim, getmaxim.ai/docs llms.txt (API nodes, agent deployment, customer support email agent guide, prompt tool calls, MCP)read 2026-09-21

In the no code agent builder, blocks connect in a visual editor, including API nodes that call third party services and loops that repeat steps; errors show at every node, and agents deploy without code changes for querying through the SDK. Deterministic API steps can sit alongside prompt steps. Branching, retries and fallback paths inside a workflow are not described.

SourceMaxim, getmaxim.ai/docs llms.txt (no-code builder nodes, loops, API nodes, deployment, error debugging)read 2026-09-21

Retrieval quality gets tested rather than supplied: the customer connects its own RAG pipeline to prompt tests, and test runs evaluate retrieval. No document ingestion, index, retrieval layer or knowledge API that grounds an agent in company data is documented.

SourceMaxim, getmaxim.ai/docs llms.txt (prompt retrieval testing, evaluate retrieval quality)read 2026-09-21

Human raters review test runs and production logs through human annotation, alongside automated evaluators, and datasets are curated from those annotations. That is review of the agent's outputs; no approval step, runtime guardrail or pause and resume control over an agent's actions is documented.

SourceMaxim, getmaxim.ai/docs llms.txt (human annotation, human annotation on logs, curate datasets)read 2026-09-21

Access control runs through organization and workspace roles for team members, workspace level RBAC, and SAML 2.0 single sign on with Okta and Google Workspace, each with its own setup guide, plus two factor authentication and a vault for secrets. Maxim Enterprise adds audit logs, and the platform can be self hosted. A trust center is linked at trust.getmaxim.ai, and no SOC 2, ISO 27001 or other attestation is named on Maxim's public pages.

SourceMaxim, getmaxim.ai/docs llms.txt (members and roles, workspace RBAC, SAML SSO, two factor authentication, vault, audit logs)read 2026-10-01

Production traffic is logged into repositories with distributed tracing of sessions, traces and spans. The platform also ingests OpenTelemetry traces over OTLP, tracks live issues, runs automated quality checks on production logs, and exports logs with their evaluation data to CSV with filters.

SourceMaxim, getmaxim.ai/docs overview and llms.txt (tracing, OTLP ingestion, exports)read 2026-09-21

Logs, sessions, datasets and versioned prompts persist as records of the customer's application. No session, workflow or long term memory that an agent reads and writes is documented.

SourceMaxim, getmaxim.ai/docs llms.txtread 2026-09-21

Deployment is either a managed service or self hosted, with full VPC isolation (Zero Touch) or a hybrid setup that places the data plane in the customer's own cloud.

SourceMaxim, getmaxim.ai/docs llms.txt (self-hosting overview, data plane deployment)read 2026-09-21

What ships ready made is evaluation material: an evaluator store of off the shelf evaluators, test presets, and guides that walk through building agents such as a customer support email agent. No ready made agents, templates or packaged workflows a buyer adopts for their own work are documented.

SourceMaxim, getmaxim.ai/docs llms.txt (evaluator store, presets, guides)read 2026-09-21

Online evaluations score production logs automatically, test runs go out on a schedule and from CI/CD pipelines, and alerts on performance and quality thresholds go to Slack and PagerDuty. Work starts on a schedule, an incoming log or a threshold without a person asking.

SourceMaxim, getmaxim.ai/docs llms.txt (online evals, scheduled runs, alerts and notifications, Slack and PagerDuty integrations)read 2026-09-21

Teams set up their own model providers before running prompts, agents and evaluators, and the playground compares output quality, cost and latency across combinations of prompts, models and parameters, with custom token pricing for accurate cost reporting.

SourceMaxim, getmaxim.ai/docs llms.txt (running your first eval, custom pricing) and overviewread 2026-09-21

Maxim publishes Python and TypeScript SDKs and public REST APIs with an OpenAPI description covering prompts, prompt tools, evaluators, workflows, datasets, alerts and integrations. Evaluations run in CI/CD through GitHub Actions, and MCP servers connect to prompts in the playground.

SourceMaxim, getmaxim.ai/docs llms.txt (SDKs, public APIs, CI/CD, MCP)read 2026-09-21

Offline evaluations run prompts, no code agents and HTTP endpoint agents against datasets, using an evaluator store and custom AI, programmatic, statistical and human evaluators. The platform also simulates multi turn conversations, compares versions in run reports, schedules test runs, runs evaluations in CI/CD through GitHub Actions, and evaluates production logs online.

SourceMaxim, getmaxim.ai/docs llms.txt (offline evals, simulation, scheduled runs, CI/CD, online evals) and overviewread 2026-09-21

Maxim covers evaluation, simulation, observability and a no code agent builder; no browser, desktop or computer control by an agent is documented.

SourceMaxim, getmaxim.ai/docs llms.txtread 2026-09-21