Agent Platform

One AI co-worker. Every employee. Every workflow.

A Claude-style co-worker — reachable from chat, CLI and MCP/API — that builds, runs and self-improves cost-aware agents on your open-source LLMs.

Synaptix AI co-worker — Ask interface answering an enterprise question with KPIs, a chart, sources and suggested follow-ups

One shell — adapts to your tenant, data and brand.

Chat · CLI · MCP · API

One agent. Three surfaces.

Same identity, memory, tools and policy — whether a VP is chatting, an engineer is scripting, or another agent is calling in over MCP.

Chat UI

For every employee

Claude-style co-worker in the browser and desktop. Ask, approve, review dashboards, kick off workflows.

SSOApprovalsDashboardsShared threads
CLI

For developers

`synaptix` in your terminal, scripts and CI. Run agents locally, pipe outputs, ship reproducible tasks.

Local dev loopCI/CDScriptableStreaming
MCP · API

For agents & apps

Expose every agent as an MCP server and REST/streaming API. Callable from Cursor, Claude, Codex and your own apps.

MCP serverREST + SSEWebhooksTyped SDKs

One identity plane. One policy engine. One audit trail across every surface.

One interface · Every kind of work

One super-agent. Eight ways to use it.

Ask, Dashboards, Agents, Workflows, Apps, Data and Governance — in front of every team and every system.

Chat & Ask

Grounded answers over your data — with citations and next actions.

RAGCitationsRole-aware

Code

A coding co-worker that reads your repos, writes PRs and ships tickets.

ReposPRsTicketsSDKs

Workflows

Visual builder for multi-step, multi-agent workflows with human gates.

Visual builderGatesPolicy packs

Agents

Scheduled, on-event and on-demand — briefings, notifiers and drafters.

ScheduledOn eventOn demand

Apps

Package workflows as internal apps — no prompt required.

Self-serveBrandedPer-team

Dashboards

Agent-generated dashboards with live sources and freshness stamps.

Live sourcesFreshnessDrill-down

Data

Governed connectors to Snowflake, Databricks, SAP, Salesforce and 200+ systems.

MCPWarehouseAppsFiles

Governance

Policy packs, PII/PHI minimization, approvals, audit and evals — built in.

PoliciesAuditEvalsRedaction
Built for open-source LLMs

Your models. Your weights. No frontier lock-in.

Every capability runs on the open models you choose — Llama, Qwen, Mistral, DeepSeek, GPT-OSS or your fine-tunes — served on the Inference Platform at frontier latency. Closed models stay optional via the AI Gateway.

LlamaQwenMistralDeepSeekGPT-OSSPhiGemmaYour fine-tunes
Per-workflow routing
Pick the right open model for each step — cost, latency, quality.
BYOM / BYOC / BYOK
Your models, your cloud, your keys. Or run fully on-prem.
Sovereign by default
No prompts, weights or data leave your perimeter.
Frontier-grade speed
Open models served at the latency agents actually need.
Self-evolving agent harness

Agents that get measurably better every week.

Every agent ships wrapped in a harness that traces, scores, tunes and governs itself. Nothing changes in production unless task-specific evaluators say it's a win — on accuracy and cost.

01 · Trace

Capture every run

Prompts, tool calls, tokens, latency, cost and outcome — logged per step, linked to the request, the user and the policy in effect.

02 · Evaluate

Task-specific evaluators

LLM-judge, rules, golden sets and human review. Each task type gets its own rubric; every run gets a score.

03 · Evolve

Tune what wins

Prompt, router, tool-choice, model and RL updates are proposed continuously — promoted only when evals beat the incumbent on quality and $/run.

04 · Govern

Versioned & reversible

Every agent version is diffed, signed, budget-capped and one-click revertible. Auditors see who changed what, when, and why it shipped.

CompoundingSame task, week-over-week: ↑ accuracy · ↓ tokens · ↓ latency · ↓ $/run. The harness is the moat.
Cost-aware by construction

Right model. Right tool. Right budget. Per step.

Every agent runs under a budget the harness enforces. Small open models handle the easy 80%; frontier models are called only when an evaluator says quality requires it. Semantic caching, batching and speculative decoding do the rest.

Per-step routing
Smallest model that passes the evaluator wins the step.
Budget guards
$/run, $/user and $/workflow caps enforced at runtime.
Semantic cache
Repeat sub-tasks served from cache with confidence checks.
Quality escalation
Auto-escalate to larger models only when scores drop.
From question to deployed agent

Ask, build, deploy, run.

Business users ask. IT composes. The harness runs, scores and improves every deployed agent — humans in the loop where it matters.

Business users ask. The co-worker answers.
01 · Ask

Business users ask. The co-worker answers.

Plain-language questions get grounded answers with KPIs, charts, sources and next actions.

IT and ops compose the workflow.
02 · Build

IT and ops compose the workflow.

Wire triggers, reads, agents, gates and writes.

Agents run on schedule, on event, on demand.
03 · Deploy & Run

Agents run on schedule, on event, on demand.

Every run observable.

Made for every enterprise

Same product. Your data, policies, brand.

Connectors, policy packs, agent library and workflows adapt to your industry on day one.

Architecture

Six layers. One control plane.

Silicon to supervisor — one API, one console. Each layer best-in-class; together they compound.

Six-layer platform diagram
Resources

Go deeper on the Agent Platform

Ready to operationalize your agents?

Talk to our team about a pilot on Synaptix Cloud or on-prem.