The Eggspert Engine

You already use AI to code. Now give it a job.

Cursor helps you write code. Copilot autocompletes it. Navique gives your AI agents actual responsibilities — reviewing every PR, triaging every issue, running test suites overnight, documenting changes, and reporting back. Not a copilot. A team.

Pipeline: nightly-review
02:00[Code Reviewer]Analyzing 6 open PRs across 3 repos...
02:01[Code Reviewer]PR #847 — 3 issues found, 2 suggestions posted
02:01[Test Engineer]Generating edge-case tests for auth-middleware...
02:03[Test Engineer]18 tests generated, 18 passing
02:03[Doc Writer]Updating API reference for 4 endpoints...
02:04[Orchestrator]Pipeline complete. Summary → #dev-updates.
The Paradigm

You have a copilot. You need a crew.

Your AI tools react. They wait for you to ask, answer one question, and forget. They don't know what happened in yesterday's PR. They can't watch your deploys. They can't run at 2am while you sleep.

AI Tools Today
Responds when you ask
Forgets everything between sessions
One model, one conversation
Can't take action — only suggest
You are the orchestrator
Eggspert Engine
Runs on schedules — hourly, daily, on every push
Persistent memory + knowledge graph across sessions
27 providers, 130+ models — mix per agent
53 tools: filesystem, shell, web, browser, git
You define the mission. Agents execute it.
Autonomous Agents

Agents with actual responsibilities.

Configure them once. They run on schedules — hourly, daily, on every push — doing real developer work. No prompting. No babysitting. Results waiting in your dashboard.

30 pre-built templates across 4 tiers — Frontier · Smart · Balanced · Fast

Agent Builder

Your agent. Your rules. Your tools.

Start from one of 30 templates or build from scratch. Every aspect is configurable through Navique's visual interface — no TOML files, no terminal commands, no code.

Choose from 30 templates or start blank
Grant specific tool access — filesystem, shell, web, browser, git
Assign skills from 60 built-in experts across 14 categories
Set primary model + fallback chain from 27 providers
Configure memory: private, shared, or read-only access
Set token budgets, spending caps, and cost alerts
Schedule on any cron pattern — or trigger from events
Clone any agent for parallel work with one click
60 Built-in Expert Skills Across 14 Categories
CI/CDKubernetesDockerAWSTerraformRustPythonTypeScriptReactNext.jsPostgreSQLRedisGraphQLSecurity AuditOAuthGitCode ReviewML EngineeringPrompt EngineeringTechnical WritingData AnalysisSentryLinearGitHub
🔍

PR Security Auditor

Custom Agent · Frontier Tier

Model

Claude Sonnet 4.6

Fallback: Gemini 2.5 Flash → GPT-4o

Tools

file_read, file_list, web_fetch, shell_exec, memory_recall

5 of 38 tools enabled

Skills

security-audit, code-reviewer, git-expert, oauth-expert

4 of 60 skills assigned

Memory

Shared (team namespace)

Reads from all agents, writes to security scope

Schedule

0 2 * * *

Every day at 2:00 AM

Budget

$2.00/day cap

Alert at 80% · Current: $0.43 today

Multi-Agent Workflows

One agent is useful. A pipeline is a superpower.

Chain agents into workflows. Your architect designs the approach. Your coder writes it. Your reviewer catches the bugs. Your test engineer validates. Your doc writer updates the reference. All coordinated automatically.

Define the workflow once. It runs every time.
Pipelines with conditional branching and loops
Fan-out parallelism — agents run simultaneously
Cron-based triggers — runs without you being online
Inter-agent messaging and task delegation
Event bus with publish/subscribe for custom triggers
01

git push to main

trigger

02

Code Review Agent

analyzing 14 changed files...

03

Test Agent

generating 23 edge-case tests...

04

Docs Agent

updating API reference for 4 endpoints...

05

Pipeline Complete

3 review comments, 23 tests added, docs updated

Task Delegation

Write the task. Assign the agent. Walk away.

This is where project management meets autonomous AI. Every task on your board is a potential agent assignment.

You're looking at your Kanban board. There's a task: “Write integration tests for the new payment webhook.” Normally you'd block out an hour, context switch into the test harness, and grind through it.

Instead, you click Assign to Agent. You pick your Test Engineer agent. It reads the task description, opens the checklist items, and starts working through them — one by one, autonomously.

You come back to a completed task, a passing test suite, and your afternoon back.

Works From Anywhere in Navique
Kanban BoardSprint ViewDashboard CardsTask DetailList View
TASK-042 · In Progress

Write integration tests for payment webhook

Assigned to: Test Engineer

🧪
Checklist
Read webhook handler and existing test patterns
Generate tests for successful payment flow
Generate tests for failed/declined payments
Generate tests for duplicate webhook events
Generate edge cases: timeout, malformed payload, replay attack
Run full test suite and report results
Agent Result

18 tests generated across 3 test files. All passing. Coverage for payment_webhook.ts: 72% → 94%. 2 edge cases flagged a missing null check in handleRefund() — fix suggestion attached.

Persistent Memory

After a month, they know your codebase.

Every conversation, every decision, every codebase pattern — your agents build a knowledge graph that connects it all. Connected intelligence that compounds over time.

Knowledge Graph — project-api
auth-middleware.ts
uses JWT validation from @lib/auth
depends on user-service for role checks
refactored in PR #847 (March 12)
staging deploy failure (March 14)
caused by Redis connection timeout in auth flow
fix: connection pool sizing in PR #852
linked to auth-middleware refactor
Team convention: error handling
all route handlers wrap in ErrorBoundary
structured errors → Sentry via errorReporter
decision recorded in ADR-017

When one agent learns something, every other agent has access to it immediately. Your researcher's findings inform your coder's context. Your architect's decisions guide your reviewer's standards.

Instant Recall

Millisecond retrieval across all sessions

Knowledge Graph

Entities, relations, semantic search

Shared Intelligence

Cross-agent knowledge sharing

Vault Integration

Obsidian-compatible, human-editable

27 Providers · 130+ Models

Your models. Your rules.

Assign different models to different agents. Claude for your architect. GPT-4o for your reviewer. Llama via Ollama for anything that should never leave your machine.

AnthropicClaude 4.6, Sonnet, Haiku
OpenAIGPT-4o, o3, o4-mini
GoogleGemini 2.5 Pro & Flash
Ollama100% local, 100% private
DeepSeekR1, V3, Coder
GroqUltra-fast inference
MistralCodestral, Large
+ 20 moreOpenRouter, Together, xAI...

$0

Platform Cost

Bring your own API keys

Real-time cost tracking per agent, per model. Budget caps and alerts.

53 Tools · 60 Skills · 33+ MCP

Not just smart. Capable.

Your agents don't just think — they act. Read and write files, execute shell commands, search the web, automate browsers, build knowledge graphs, communicate across 40 messaging platforms.

Filesystem & ShellRead, write, copy, move, execute — with audit trails
Web & SearchHTTP fetch, multi-provider search, URL screenshots
Browser AutomationNavigate, click, type, screenshot — full Playwright
Knowledge GraphEntities, relations, semantic search, graph export
Inter-Agent CommsSpawn agents, delegate tasks, direct messaging
MCP ProtocolConnect to 33+ servers — or expose agents as MCP tools
Scheduling & TriggersCron jobs, event patterns, configurable fire limits
Budget ControlPer-agent, per-model caps. Alerts before limits hit.
40 Channel AdaptersSlack, Discord, Telegram, Teams, email, and 35 more
16 Security Layers

Powerful agents. Accountable agents.

Your agents have real power — filesystem access, shell execution, web requests. What makes them trustworthy is 16 independent security layers ensuring every action is audited and every sensitive operation requires your approval.

Tamper-Proof Audit Trail

Every agent action is recorded in a Merkle hash-chain. Modify a single entry and the entire chain breaks. You always know exactly what happened, when, and why.

Approval Gates

Agents ask before doing anything you mark as sensitive — deploying, deleting, spending, modifying infrastructure. Nothing destructive happens without your explicit approval.

Taint Tracking & Signed Manifests

Information flow labels propagate through execution — secrets are tracked from source to sink. Every agent identity and capability set is cryptographically signed with Ed25519.

Your Machine, Your Data

Everything runs locally. Your code, credentials, and conversation history never touch our servers. The only external calls are the ones you authorize to your chosen AI providers.

Plus: WASM-metered tool execution · Prompt injection scanning · SSRF protection · Secret zeroization · Capability-based access · Loop detection with circuit breakers · Path traversal prevention · Session repair · Rate limiting

Nightly Output

What ships while you sleep.

Developers using Navique's agent workflows are waking up to completed work. Every morning. Automatically.

6 PRs reviewed

With line-by-line comments and quality scores

47 tests generated

For yesterday's auth refactor — all passing

12 issues triaged

Severity assigned, owners suggested, linked to PRs

API docs updated

4 new endpoints documented from merged PRs

Weekly insight ready

Commits, velocity, costs, blockers — summarized

$0.43 spent

Total agent cost for the night — tracked per model

All configured through the Navique GUI. No config files. No terminal setup. Point, click, schedule.

Give Your AI
A Job.

7-day free trial. The full engine. No credit card. No limits. Then $99 to own it forever.

Requires macOS 15.0+ · Apple Silicon & Intel

Download for macOS