The System of Record for AI Agent Governance
Trust, cost, and scale — the three barriers to enterprise AI adoption. Aiqarus solves all three: governance for the CISO, smart cost routing for the CFO, and multi-tenant orchestration for the CTO.
Core Capabilities
Everything you need to build production-ready AI agents
Goal Layer
Goals persist beyond individual runs—they're the durable intent that survives failed attempts. When one approach doesn't work, the goal remembers what was tried and replans.
- Persistent intent
- Intelligent replanning
- 4-layer evaluation
Agent Builder
Create agents with AI assistance. Define behavior, set constraints, connect integrations.
- AI-assisted creation
- Version control
- Natural language config
Execution Engine
The Think-Decide-Act-Observe loop with safety constraints and real-time monitoring.
- Safety constraints
- Decision points
- Human-in-the-loop
Memory System
5-level hierarchical memory with automatic lesson extraction and injection.
- Hierarchical lessons
- Episodic learning
- Cross-agent knowledge
Workflow Orchestration
DAG-based multi-agent workflows with parallel execution.
- Parallel execution
- Conditional branching
- Goal-based triggers
Integrations
44+ enterprise connectors across 6 operational domains with MCP support.
- 44+ connectors
- MCP protocol
- Custom SDK
Security & Audit
SHA-256 hash-chained audit with Ed25519 attestations.
- Immutable traces
- Chain verification
- aiq-trace-v1 format
Key Differentiators
What Aiqarus does that no competitor offers in combination
9-Role Smart Model Routing
Every task is classified by function and routed to the cheapest capable model automatically. Title generation doesn't need GPT-4 — it runs on a model that costs 1/50th as much.
~6x LLM cost reduction
$3,400/mo → $550/mo for a 40-agent deployment. The difference between a CFO approving the AI budget and killing it.
BYOK (Bring Your Own Key)
Customers use their own API keys for OpenAI, Anthropic, Google, and Azure. Aiqarus never touches their LLM spend.
Zero LLM COGS
Enterprise procurement loves it — their legal team already has DPAs with these providers. No new vendor risk. No markup, no middleman.
Cross-Model Evaluation
When an agent produces output, a model from a different provider family evaluates it. Claude evaluating GPT-4 output. Gemini evaluating Claude output. Never self-evaluating.
4-layer evaluation pipeline
Schema validation → semantic evaluation → business rules → cross-model quality check. Every output is scored before it reaches the user.
Intake Agent
Every competitor assumes clean, structured input. Real enterprise work starts with messy documents — legal briefs, annotated screenshots, email threads with embedded files.
Unstructured → structured
Vision models process messy client materials into actionable tasks. No manual parsing required. Natural entry point for teams to try Aiqarus.
The TDAO Loop
Transparent Reasoning at Every Step
Watch agents Think, Decide, Act, and Observe in real-time. Every step recorded in a tamper-proof audit trail.
THINK
Analyze context and understand the task
DECIDE
Choose the best action to take
ACT
Execute the chosen action
OBSERVE
Record outcome and learn
Cryptographic Verification
Every step recorded in SHA-256 hash-chained logs. If anyone modifies a record, the chain breaks. Auditors can verify the complete history with cryptographic proof.
Features
Built for Enterprise
Architecture decisions that prioritize correctness, traceability, and safety
Declarative Definitions
Agents are configuration, not code. Version, reproduce, and audit everything.
Built-in Safety
Max steps, max duration, token limits. Prevent runaway agents automatically.
Multi-Tenant by Design
Complete data isolation between organizations. No cross-tenant leakage.
API-First Architecture
Full GraphQL API. Embed agents into your existing applications.