Build the agent. Keep the control layer.
Praxa provides the runtime pieces agentic products keep rebuilding: model routing, tools, memory, browser execution, approvals, and reviewable results.
Build with PraxaFor teams building production agent workflowsFeatures
Deliberate tool admission
Expose only the tools a runtime should use. Cloud, voice, and on-device catalogs remain intentionally scoped.
Context with provenance
Attach the right context to the request. Keep sources and memory boundaries visible through execution.
One working context across your personal agent, organizational memory, background missions, and connected control.
A production agent loop in three layers
Define what the agent may use, let the runtime prepare the work, and require the right human decision before anything consequential commits.
01. Admit
Scope the runtime. Register only the models, tools, connectors, and memory the use case should access.
02. Execute
Run with persistent context. Route the model, call tools, browse when needed, and keep mission state visible.
03. Review
Return a reviewable outcome. Surface sources and request approval before the system commits a consequential action.
A control layer for production agents
Compose models, tools, memory, and execution surfaces without collapsing their safety boundaries.
Discuss an integrationRoute supported OpenAI, Anthropic, and Google models through one explicit product policy.
Register tools deliberately for cloud chat, realtime voice, on-device, or browser execution.
Let the agent prepare the work, then stop at the policy boundary before a committing action.
- Object-shaped tool schemas
- Runtime tool admission
- Fail-closed approval gates
- Reviewable tool output
Contract before prompt
Treat schema, admission, approval, and rendering as executable product contracts—not instructions a model may choose to follow.
One traceable execution pipeline
Carry a request from model routing through tools, persistent mission state, approval, and a reviewable result without losing the boundaries between them.
Request to reviewed result
Route the model, admit the right tools, preserve mission context, and surface the result with the evidence a person needs to decide.
- Policy-aware model routing
- Purpose-scoped worker execution
- Approval-gated commitments
Persistent mission state
Keep execution status and handoff context attached to long-running work.
Approval boundaries
Refuse committing actions when the required human approval is missing.
Cloudflare runtime
Route API workloads across purpose-scoped workers and service bindings.
Source retention
Keep citations and tool results available for review with the outcome.
Built around responsibility, not a demo reel
Praxa is designed to make agentic work more useful without hiding how it reached a result or who approved an action.
Autonomy is useful when the system can show its work, respect the boundary, and stop when approval is required.
How Praxa handles approvalFail closed
No required approval, no committed action.
Sources, mission state, memory, and approval details stay visible so a result can be checked instead of merely trusted.
Praxa can prepare and coordinate the work while consequential commitments remain explicitly human-approved.
Build the product, not another control layer
Bring your use case. Praxa provides the agent runtime, model access, tools, memory, approvals, and execution boundaries.
Talk to the platform team