Enterprise AI Agent Platform
The system of record
for AI agent
governance.
Enterprise AI agents with cryptographic audit trails, smart cost routing, and multi-tenant orchestration.
Trust
The CISO signs off.
Full audit trail, human-in-the-loop approvals, 4-layer output evaluation. Every decision cryptographically attested.
Cost
The CFO signs off.
BYOK — no LLM markup. Smart 9-role model routing cuts LLM spend ~6x. Full spend visibility per agent, per team.
Scale
The CTO signs off.
44+ integrations across 6 domains. Multi-agent DAG orchestration. 5-level memory that compounds with every run.
The Problem
When AI fails, “the model made a mistake” isn't an acceptable answer.
Regulators need proof. Auditors need trails. Your organization needs accountability.
And when humans alone handle it...
The answer isn't AI alone or humans alone. It's bounded autonomy—AI that acts within defined authority and escalates at its limits.
Bounded Autonomy
AI that knows when not to act.
The difference between useful and dangerous AI is knowing its limits.
Authority Boundaries
Agents can only act within explicitly granted capabilities. No scope creep, no surprise actions.
Risk Thresholds
High-impact actions automatically pause for human approval. Risk is calculated, not assumed.
Confidence Limits
Uncertain decisions escalate rather than guess. Agents know what they don't know.
Mandatory Escalation
Structurally required to pause when limits are reached. Not optional, not bypassable.
Goal-Driven Architecture
Goals persist. Runs are ephemeral.
Traditional agents execute tasks. Aiqarus agents pursue outcomes with explicit success criteria, replanning when approaches fail.
Define Goals, Not Tasks
Goals capture what you're trying to achieve, not how to achieve it. Success criteria are explicit and measurable.
Intelligent Replanning
When an approach fails, agents try alternative strategies. Goals track which methods worked and which didn't.
4-Layer Evaluation
Schema validation, semantic evaluation, business rules, and goal criteria. Agents know when they've truly succeeded.
Execution Engine
Think. Decide. Act. Observe.
The TDAO loop runs within each Goal until success criteria are met or the goal is replanned.
Think
Analyze context, retrieve memories, consider constraints
Decide
Generate options, evaluate trade-offs, assess confidence
Act
Execute with safety limits, pause for approval if high-risk
Observe
Record outcome in hash-chained audit, update memory, evaluate
5-Level Memory Hierarchy
Agents that learn from every execution.
After each run, lessons are automatically extracted and scored. The next similar task benefits from what worked before. Organizational knowledge accumulates.
- Working → Short-term → Episodic → Semantic → Organizational
- Automatic lesson extraction from every run
- Hierarchical matching: agent → template → product → org-wide
- Lessons injected into prompts based on context
Memory Flow During Execution
BEFORE RUN
Query lessons, load semantic memory
DURING RUN
Working memory active, TDAO loop
AFTER RUN
Extract lessons, record episode
Built for Compliance
Cryptographic proof of every decision.
- SHA-256 hash-chained audit with Ed25519 attestations
- aiq-trace-v1 format with chain anchor proofs
- Database triggers prevent modification or deletion
- One-click chain verification for auditors
Hash-Chained Audit Trail
Chain intact • Tamper-evident
Why Not Just...
There are other approaches. Here's why they fall short.
vs ServiceNow
$200K+/yr, 12-month deploy
Enterprise governance exists but at enterprise cost and timeline. Aiqarus delivers the same compliance posture in weeks, not quarters.
vs OpenAI Frontier
Single-provider lock-in
Strong governance — but locks you into OpenAI models with OpenAI pricing. No multi-provider BYOK, no smart cost routing, no channel partner distribution.
vs LangChain / CrewAI
Framework, not platform
You still build governance, audit trails, memory, and compliance yourself. That's months of infrastructure work before any business value.
vs Beam AI
No channel model
Enterprise-direct only. No multi-tenant resale, no white-label, no partner ecosystem. Every new customer requires direct sales effort.