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Parinita Flow

Sovereign automation — MCP-native, blockchain-audited, 400+ connectors. Replaces Zapier / n8n / Make / Workato for regulated enterprises.

Flow is the sovereign agentic automation platform — a four-layer stack implementing Model Context Protocol (MCP) as a first-class sovereign gateway. It replaces Zapier, n8n, Make, Activepieces, Workato, and UiPath for enterprises that can't accept legacy automation's four failure modes: stateless tool execution, mutable audit trails, sovereign data-boundary violations, and single-agent design.

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Capabilities

What it does

  • Four-layer sovereign stack

    L1 Tool Library (400+ MCP tools on Plane 3/7). L2 MCP Gateway (Parinita MCP Server on P8 AmpereOne). L3 Policy & Identity (Crucible per-call workload identity, eBPF/XDP enforcement, Corridor sovereign egress). L4 Audit (QBFT Chrysalis transaction per tool call).

  • 400+ MCP connectors

    TypeScript/Python tools for enterprise applications, named under a strict convention (flow_salesforce_create_opportunity, flow_github_create_issue, flow_postgres_execute_query) to prevent namespace collisions across multi-server MCP configs.

  • Per-call workload identity

    Every tool call carries an explicit agent identity scoped to capability bounds. eBPF/XDP enforces below the OS — architecturally bypass-impossible.

  • Six-stage execution pipeline

    Client auth (OAuth 2.1 or Crucible tokens) → workload-identity resolution → policy evaluation → credential retrieval (Sovereign Credential Vault on P5) → connector dispatch (with consistency policy) → Chrysalis audit. Every stage hashed.

  • Audit entry the regulator wants

    Each Chrysalis entry includes entry_id, block_height, timestamp_ns, pop_id, agent_id, tool_name, policy_context, input_hash, output_hash, execution_ms, outcome, and validator signature. HIPAA audit prep collapses from weeks to a query.

  • Consistency by classification

    ITAR / HIPAA / Financial workloads use strong consistency with synchronous Chrysalis acknowledgment. Standard workloads use eventual consistency. Pick by data class, not by code path.

How it works

The four layers:

  • L1 — Tool Library (Flow Connectors). 400+ TypeScript/Python MCP tools for enterprise applications running on Plane 3 / Plane 7.
  • L2 — MCP Gateway. The Parinita MCP Server on P8 AmpereOne — a sovereign MCP host with Streamable HTTP transport, dynamic tool registration, OAuth 2.1 / Crucible token authentication.
  • L3 — Policy & Identity. Crucible per-call workload identity, ConnectX-7 eBPF/XDP hardware enforcement, Corridor sovereign egress.
  • L4 — Audit. Chrysalis blockchain immutable QBFT audit per tool call.

Tools follow a strict naming convention: flow_{connector}_{action}_{resource} (e.g., flow_salesforce_create_opportunity, flow_github_create_issue, flow_postgres_execute_query) to prevent namespace collisions in multi-server MCP configurations.

Every tool call traverses a six-stage sovereign execution pipeline:

  1. Client authentication — OAuth 2.1 with PKCE for external clients; Crucible-issued workload-identity tokens for Parinita-native agents like Conduit and Atma.
  2. Workload-identity resolution — mapping the OAuth token to a specific agent identity with capability bounds.
  3. Policy evaluation — eBPF/XDP on ConnectX-7. Below the OS, architecturally bypass-impossible.
  4. Credential retrieval — from the Parinita Sovereign Credential Vault on Plane 5 NVMe.
  5. Connector dispatch — with consistency policy: ITAR/HIPAA/Financial = strong consistency with synchronous Chrysalis acknowledgment; Standard = eventual consistency.
  6. Chrysalis audit — entry includes entry_id, block_height, timestamp_ns, pop_id, agent_id, tool_name, policy_context, input_hash, output_hash, execution_ms, outcome, validator_signature. ECDSA today; ML-DSA-65 (FIPS 204) post-quantum upgrade targeted 2027.

When to use it

  • Regulated workflows where you need to prove what an agent did, in what order, with what evidence — KYC, claims processing, clinical decision support, model promotion pipelines.
  • Long-running orchestrations that mix automation with human review and need a durable handoff between stages.
  • Multi-agent pipelines where each agent owns one stage and you need a verifiable trail across stages.

What it isn’t

A general-purpose BPM tool. Flow is tuned for AI workflows: agents, models, and tools as primitives, with a cryptographic audit substrate. If your workflows don’t touch AI, a traditional workflow engine will probably feel lighter.

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Part of the Parinita AI Edge

Bring Parinita Flow into your stack.

Every Parinita product runs on the same 9-plane fabric across 101 edge POPs. Talk to us about a pilot, or see how the pieces fit together.

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