Turn Tokens into
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Agent Runtime · Model-tool collaboration · Multi-Agent task orchestration
From single-turn conversations to complex long tasks, a complete Agent execution infrastructure that is observable, traceable, and cost-controlled.
The underlying infrastructure for Agent execution
All the runtime capabilities needed to run long-lived Agents reliably, ready out of the box, no need to build it yourself.
Session & Context Management
Persistent storage and efficient retrieval of multi-turn context, with long-context compression and truncation so the model always sees the most relevant information window.
- Multi-session concurrency isolation
- Context compression & summarization
- Cross-task memory injection
Memory & State Persistence
A layered memory architecture with independent working, episodic, and semantic memory. Agent state is fully persisted, resuming seamlessly after restart without losing progress.
- Three-tier memory storage
- State snapshots & hot recovery
- Memory eviction & update policies
Code Sandbox & Resource Governance
A securely isolated code execution environment with multi-language runtimes and strict CPU, memory, and network limits. Agent lifecycles are managed precisely, with idle resources reclaimed automatically.
- Multi-language sandbox isolation
- Fine-grained resource quotas
- Lifecycle & timeout governance
Connecting model capabilities to the outside world
Standardized protocol access, tool registration & discovery, reliable Tool Calling, and intelligent small-large model routing.
MCP / A2A Protocol Access
Native support for Model Context Protocol and Agent-to-Agent communication, seamlessly connecting to third-party tool ecosystems and cross-Agent collaboration, with standardized integration that reduces friction.
Tool & Skill Registration and Discovery
A unified tool registry with self-describing schemas, version management, and dynamic discovery. The Agent runtime loads skill modules on demand, with hot-swap and canary release support.
Tool Calling Reliability & Structured Output Validation
Automatically validates model output against tool schemas, detecting and repairing format errors. Supports JSON Schema / Pydantic dual-mode validation, significantly improving structured-output success rates.
Retrieval & RAG Components
Built-in vector, sparse, and hybrid retrieval pipelines with rerank and contextual relevance filtering. Deeply integrated with the inference layer's Prefix Cache to reduce duplicate retrieval cost.
Small-Large Model Collaborative Routing
Automatically routes between large and small models based on task complexity, latency requirements, and budget. Simple subtasks use lightweight models for speed and cost, while critical decision steps call flagship models for quality, a dynamic cost-quality tradeoff transparent to the business layer.
Engineered Delivery for Complex Tasks
From multi-Agent collaboration to long-task scheduling, production-grade orchestration that makes complex workflows reliable, controllable, and auditable.
Multi-Agent Collaboration & Task Decomposition
Native support for the Orchestrator-Worker pattern: the main Agent decomposes complex tasks into parallelizable subtasks, dispatches them to specialized Agents, and aggregates results back into the main flow. Supports dynamic topology with on-demand scaling of sub-Agents.
Workflow / State Machine / DAG Orchestration
Three orchestration paradigms to choose from: linear workflows for sequential processes, finite state machines for complex branching, and DAGs for arbitrary concurrent dependency topologies. One unified runtime with seamless hybrid orchestration.
Long-Task Scheduling · Resume · Idempotent Retries
Real-time snapshots of task progress; after a network drop or model timeout, execution resumes automatically from the checkpoint without restarting. All steps are idempotent, so retries have no side effects.
Human-in-the-loop
Insert human approval nodes at any step: the Agent pauses and sends a notification, then continues after approval. Supports batch approval and delegation rules, balancing efficiency and compliance.
Task-Level SLO: Completion Time + Cost Ceiling
Set a maximum completion time and Token budget per task; the runtime monitors in real time and alerts, automatically degrading or terminating before limits are exceeded to guarantee predictable delivery.
See what happens inside your Agent
Full-chain tracing, cost attribution, quality evaluation, and end-to-end optimization with the inference platform.
Full-Chain Trace & Token Cost Attribution
Every Agent call, tool execution, and model inference generates a structured Span, forming a complete call tree. Token consumption and cost are precisely attributed across task, step, and user dimensions, with transparent, auditable billing.
- OpenTelemetry-compatible output
- Multi-dimension Token & cost attribution
- Real-time call-chain visualization
Success Rate & Quality Evaluation Sets
Built-in task success-rate statistics and a quality evaluation framework with custom evaluation sets, regression tests, and A/B experiments. Automatically runs evaluation comparisons before model upgrades or prompt changes to prevent quality regressions.
- Custom evaluation-set management
- Auto-triggered regression tests
- A/B experiment comparison reports
End-to-End Optimization with Inference
Deep coordination between the Agent and inference layers: reuse the KV of identical system prompts via Prefix Cache to cut recomputation; shorter sequences after context compression reduce decoding latency; the two layers work together to optimize cost and latency simultaneously.
- Cross-task Prefix Cache reuse
- Context-compression-linked decoding
- End-to-end cost attribution loop
Explore Agent Platform Solutions
Whether you're building a customer-service Agent, a coding assistant, or complex business automation, our team is ready with custom solutions and technical support.