Built for business
Enterprise-grade AI agents with the reliability, security, and scalability your business demands.
Tool Calling
Agents don't just generate text—they take action. Define tools and let agents decide when and how to use them. Execute code, call APIs, read files, query databases.
- ✓ Function definitions
- ✓ Automatic parameter extraction
- ✓ Error handling & retries
- ✓ Tool chaining
agent.tools = [
{ name: 'search', fn: searchWeb },
{ name: 'read_file', fn: readFile },
{ name: 'run_code', fn: executeCode },
{ name: 'send_email', fn: sendEmail }
]
// Agent decides which tool to use
> "I'll search for the latest data..."
> calling: search("AI trends 2025")Task Scheduling
Agents break down complex goals into executable steps. They create their own task queues, prioritize work, and execute autonomously until the goal is achieved.
- ✓ Automatic task decomposition
- ✓ Priority queues
- ✓ Dependency tracking
- ✓ Progress checkpoints
Goal: "Write a blog post about AI" Tasks created: [✓] Research current AI trends [✓] Outline main sections [→] Write introduction [ ] Write body sections [ ] Write conclusion [ ] Review and edit
Persistent Memory
Agents remember. Short-term working memory for current tasks. Long-term memory that persists across sessions. Semantic search to recall relevant context.
- ✓ Working memory (current context)
- ✓ Long-term storage
- ✓ Semantic retrieval
- ✓ Memory consolidation
memory.store({
type: 'fact',
content: 'User prefers concise answers',
importance: 0.9
})
// Later, automatically recalled
> "Based on your preference for
concise answers, here's a summary..."Self-Reflection
Agents evaluate their own work. Before returning a response, they review it for errors, completeness, and quality. They catch mistakes and improve outputs autonomously.
- ✓ Output validation
- ✓ Error detection
- ✓ Quality scoring
- ✓ Iterative refinement
[thinking] Generated response... [reflect] Checking for errors... [reflect] Found issue: missing source [action] Adding citation... [reflect] Quality score: 0.92 [complete] Response ready
Enterprise Security
Built with security-first architecture. Your data is encrypted, access is controlled, and every action is audited. Meet compliance requirements without compromise.
- ✓ SOC2 Type II compliant
- ✓ End-to-end encryption
- ✓ Role-based access control
- ✓ Complete audit trails
Security Features: ├── Encryption at rest (AES-256) ├── Encryption in transit (TLS 1.3) ├── API key rotation ├── IP allowlisting ├── SSO / SAML support └── Audit logs (90 day retention)
Infinite Scale
Auto-scaling infrastructure that grows with your workload. Handle traffic spikes without manual intervention. Pay only for what you use.
- ✓ Auto-scaling to demand
- ✓ Multi-region deployment
- ✓ Load balancing
- ✓ Zero-downtime deploys
Scaling: auto ├── Min instances: 2 ├── Max instances: unlimited ├── Scale up: <100ms ├── Regions: us-east, eu-west, ap-south └── Current load: 847 agents active [■■■■■■■■░░] 78% capacity
High Availability
Built for mission-critical workloads. Automatic failover, health monitoring, and redundancy ensure your agents stay online when it matters most.
- ✓ 99.9% uptime SLA
- ✓ Automatic failover
- ✓ Health monitoring
- ✓ Disaster recovery
Status: operational ├── API Gateway [■■■■■] 100% ├── Agent Runtime [■■■■■] 100% ├── Database [■■■■■] 100% └── Storage [■■■■■] 100% Uptime (30d): 99.98% Incidents: 0
Zero Lock-in
No lock-in to any LLM provider. No lock-in to our cloud. Run locally, self-host, or use Apteva Cloud. Switch anytime with zero code changes.
- ✓ Any LLM: Claude, GPT, Gemini, Llama
- ✓ Any deployment: local, self-hosted, cloud
- ✓ Hot-swap providers without downtime
- ✓ Export your data anytime
# Run locally $ apteva start # Self-host with Docker $ docker run apteva/apteva # Or use Apteva Cloud # cloud.apteva.ai Same code. Same API. Your choice.
Message-Based Communication
Agents communicate through a simple message-passing protocol. No shared memory. No race conditions. Clean boundaries between agents.
- ✓ Async messaging
- ✓ Request/response
- ✓ Pub/sub patterns
- ✓ Message persistence
agent_01 -> agent_02: task.assign agent_02 -> agent_01: task.accepted agent_02 -> agent_03: subtask.delegate agent_03 -> agent_02: subtask.complete agent_02 -> agent_01: task.complete
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