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Agent Runtime
Governed.

Forecast and monitor what every agent run costs, enforce what each one can touch, and coordinate execution across the models, frameworks, and providers you already use.

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Org Overview

$48.2k tracked · 6.3% under budget
Cost Efficiency
Optimization: $8.4k/mo savings available
Most Expensive: Customer Support Agent (18%)
6.3% under budget pace
↓ 9.5% vs budget
Forecast Accuracy
Runs Forecast: 1,240 priced before execution
Largest Drift: Customer Support Agent (context growth)
94.2% ran within forecast
↓ 3.8% mean drift
Run Quality
Critical Workflow: Customer Onboarding
Failure Cause: Retry loop after CRM validation
27.9% failed executions
↑ 4.8x retry rate
Reliability
Provider Failovers: 18 automatic recoveries
Current Incident: Azure OpenAI latency degradation
99.21% platform availability
↓ 0.77% below SLA target
Live Platform Activity
Agents runningTool callsTokens

Agents running

642

Tool calls / min

1,940

Tokens

1.84B

Retries

2.1%

Policy violations

12

Violations
00:0004:0008:0012:0016:0020:00now
Runtime TopologyHealth · cost · latency · violations, per layer
Agents642
$48.2k1.9s12 violations
Workflows148
$21.4k4.8s3 degraded
Models11
1.84B tokens640ms0 outages
Providers(6)
OpenAI$21.1k · 580ms
Claude$14.8k · 610ms
Azure OpenAI↑ 22% latency
Gemini$5.2k · 520ms
Tools & MCP(128 · 58 servers)
Salesforce412k calls
SAP208k calls
Production DB18 agents scoped
Slack · GitHub96k calls
Memory & state(4 stores)
Pinecone1.2B vectors
Redis4ms p99
S3 Archive18.4 TB
Postgres98% cache hit

642

Agents

128

Tools

241

Skills

390

Prompts

11

Models

6

Providers

58

MCP servers

148

Workflows

17

Teams

42

Projects

9

Tenants

3

Environments

Our pillars

Nasiko governs what your agents cost, touch, and run.

Three questions determine an agent’s production status: spending, access, and execution. Nasiko addresses all from one place, starting with cost, as it’s the first to break.

01 · Inside TokenOps

One system. Three jobs.

Monitor, Optimize, Control: every cost feature is one of the three. A dashboard tells you what happened; TokenOps decides what happens next.

How routing cuts spend

Retry loop ×4 detected

Request halted due to unexpected rise in cost

Actual $11Saved $27Projected $38
  • Monitor

    See what a run should cost before it starts, then watch the actual spend the moment it happens: by team, agent, and model, down to a single run. Cost stops being a month-end surprise and becomes an operating metric.

  • Optimize

    Each request routes to the right model at boundaries you set. A small model handles the easy parts; a strong one is called in only where it earns its cost, so the bill drops without the output getting worse.

  • Control

    A risk monitor watches every active run for loops, overruns, and runaway retries. Cross the line you drew, and the controller halts or escalates the run in flight, not after the invoice.

02 · Inside Policy & Governance

Every action checked. Every decision on the record.

Know what every agent is doing, control what it can touch, and prove it afterward. Policy is enforced at the gateway as each call happens, not reconstructed in a postmortem.

How enforcement works
  • See

    Every agent mapped to its tools, its data, and its owner. Model and provider allowlists, budget policies, and high-risk workflows, all visible on one dashboard before anything goes wrong.

  • Enforce

    Policy takes effect at the gateway, on the call: guardrails, tool and data restrictions, budget and retry ceilings, approvals for sensitive actions, and a kill switch when an agent has to stop now.

  • Prove

    A complete history of violations, exceptions, and blocks, each explained in plain terms: audit-ready reporting for compliance, risk, and executive review.

Permissions playground

Per agent · per tool
gmail__search_email
forwarded with your credentials
calendar__create_event
held for your approval
github__merge_pr
not in the tool list
In the agent's tool list
2 of 3 tools
Credentials held by
the gateway
Changes take effect
next call

Stances are enforced twice: once when tools are listed, again when each call lands.

Live: the gateway's per-tool permission model. A blocked tool isn't refused; it was never offered.

03 · Inside the Multi-Agent Harness

Author once. Run anywhere. See everything.

Compose multi-agent workflows across the models, providers, and frameworks you already run. Nasiko coordinates them without replacing any of it: switching providers mid-flow, carrying state across steps, tracing every handoff.

Explore the harness

Workflow trace

Customer Onboarding · 5 roles · 3 providers
plannerclaude · anthropic$0.04
retrievergpt-4o · azurererouted: provider latency$0.11
analystgemini · vertex$0.3861% of run cost
executorlanggraph · in-house$0.02
criticclaude · anthropic$0.07
Forecast
$0.58
Actual
$0.62
Handoffs traced
6 of 6
  • Author

    Compose agents into workflows (roles, tools, memory, and prompts) across frameworks, vendors, and teams. Attach policy and budget at authoring time, and simulate the cost before anything launches.

  • Execute

    One run can span multiple model providers and harnesses. Nasiko coordinates the handoffs: provider switching, fallback routing, and state that survives every step, with cost and policy checked in flight.

  • Monitor

    Harness-level traces of every tool call, retry, loop, and handoff. Find the expensive step, see why a run failed or slowed, and compare what it cost against what it produced.

The platform

One platform under all three.

Every pillar runs on the same ground: a registry that names every agent, a gateway that enforces every decision, and observability that explains every run.

See the platform
  • Agentic Registry

    Names every agent, its owner, and its version, so cost and traces have something to attach to.

  • Identity & Access

    Every agent mapped to an owner, every permission to a role, with tenant isolation built in.

  • Gateway & Routing

    The governed entry point. Each request routed to a chosen model; policy takes effect on the next call.

  • Observability

    Full traces: what ran, what it cost, why it retried, and why each model was chosen.

  • Developer Workflow

    CLI, API, SDKs. Agents shippable like software, governed like infrastructure.

  • Enterprise Connectivity

    Your APIs, MCP servers and model providers, connected, not replaced.

For developers

Built CLI-first.

One command takes an agent from your machine to a running deployment: registered, traced, and on the books, with zero code changes. Nasiko works with the frameworks, MCP servers, and model providers you already use.

Read the quickstart

$ curl -fsSL https://registry.nasiko.dev/r/nasiko/install-ee | bash

FAQ

Frequently asked questions

A router picks a model. Nasiko forecasts what a request will cost before it runs, routes it, watches it live, and steps in when it goes wrong, then writes every decision to a ledger. Routing is one step of six.

Tracing tells you what happened after the run. Nasiko acts during it. Observability is the record; Nasiko is the control. You get both, but the point is the intervention, not the report.

A cap stops work when a number is hit, which punishes the teams using AI well. Nasiko governs each run instead: it forecasts, routes to the model that earns its cost, and halts only the runs that are actually failing. Control without a blunt ceiling.

The call never gets through. Policy is enforced at the gateway as each request lands: a blocked tool isn't even in the agent's tool list, sensitive actions are held for your approval, and a kill switch stops a run outright. Every decision is written to the audit trail, so you can prove what happened, not just investigate it.

No. Nasiko connects to the models, frameworks, MCP servers, and agent harnesses you already run, and coordinates across them. It never asks you to rip anything out.

Talk to us about a plan that fits your run volume. Pricing scales with the runs Nasiko governs, not seats.

Every agent.
Accounted for.