The engine behind every AI employee
AgentLabz operates its own agent runtime, orchestration layer, retrieval stack, and GPU-accelerated inference path. Every role a customer deploys runs on the same production platform.
Sub-second
Voice turn latency target
Continuous
In-flight request batching
Cached
KV cache reuse across agent steps
Autoscaled
Pools segmented by latency class
Ten systems, one platform
Each subsystem is independently versioned, observable, and deployable. Customers configure roles; the platform handles execution, safety, and scale.
AI Agent Runtime
A deterministic execution engine for long-running agents: step-level checkpointing, sandboxed tool execution, retries with idempotency keys, and hard token and time budgets per run.
Agent Orchestration
Decomposes an objective into a dependency graph, dispatches specialised agents, and supervises the run with back-pressure, cancellation, and deadlock detection.
Knowledge Retrieval
Hybrid dense and keyword retrieval with re-ranking, permission-aware filtering, chunk freshness scoring, and citations attached to every generated claim.
Memory
Short-term conversation state, episodic run history, and durable semantic memory with per-tenant isolation, decay policies, and explicit write approvals.
Tool Calling
Typed tool schemas with JSON-schema validation, scoped credentials, per-tool rate limits, dry-run mode, and human approval gates on privileged actions.
Workflow Engine
Event, schedule, and webhook triggers composed into durable state machines with branching, retries, compensation steps, and replay from any checkpoint.
Inference Layer
A provider-abstracted serving path with per-agent model routing, fallbacks, streaming, prompt and KV cache reuse, and request batching — designed to run on GPU-accelerated capacity.
Voice AI
Streaming speech recognition, endpointing, barge-in handling, and neural speech synthesis pipelined to keep a conversational turn inside a single latency budget.
Observability
Trace-level visibility over every run with OpenTelemetry-style spans, evaluation against golden question sets, regression detection, and one-click run replay.
Security
Tenant isolation, role-based access control, encrypted secrets, scoped tool permissions, and an audit record of every configuration change and agent action.
API Infrastructure
A resource-oriented REST surface with streaming responses, signed webhooks, scoped API keys, and a published OpenAPI specification.
From a message to a cited answer
A single request traverses the gateway, runtime, planner, memory, retrieval, and accelerated inference before a token ever reaches the user — every hop traced and budgeted.
Versioned
Every agent, prompt, and tool schema is versioned and diffable.
Isolated
Tenant data, keys, and vector namespaces never share a boundary.
Budgeted
Hard token, time, and spend ceilings enforced by the runtime.
Built for GPU-Accelerated AI
Agent workloads are inference-heavy: many short model calls per task, plus speech and retrieval inside the same turn. We tune the serving path end to end instead of passing requests through to a third-party endpoint.
Dynamic batching
Incoming requests are grouped in-flight so GPUs stay saturated without holding interactive traffic behind long generations.
KV cache reuse
Shared system prompts and repeated agent context are cached across steps and turns instead of being recomputed each call.
Continuous batching
Finished sequences leave the batch immediately and new ones join, keeping utilisation high under bursty agent workloads.
Streaming inference
Tokens are emitted as they are produced, so the first word reaches the user long before the answer finishes.
Tensor parallelism
Large open-weight models are sharded across multiple GPUs to fit memory and cut per-token latency.
Embedding acceleration
Bulk embedding and cross-encoder re-ranking run on GPU, making large knowledge bases practical to re-index frequently.
Speech inference
ASR and TTS share accelerated capacity with the LLM path so a voice turn completes inside one budget.
Multi-GPU scheduling
Workloads are placed by latency class and memory footprint across mixed pools, with autoscaling per queue depth.
Sub-second
Voice turn latency target
Platform engineering target, not a published benchmark.
Continuous
In-flight request batching
Platform engineering target, not a published benchmark.
Cached
KV cache reuse across agent steps
Platform engineering target, not a published benchmark.
Autoscaled
Pools segmented by latency class
Platform engineering target, not a published benchmark.
Supported AI models
Choose a model per agent, or per step. The router handles fallback, cost ceilings, and latency classes so you can change models without rewriting an agent.
Llama
Open weight · Meta
Routing: Default for high-volume, cost-sensitive steps
DeepSeek
Open weight
Routing: Planning and long chain-of-thought steps
Qwen
Open weight · Alibaba
Routing: Multilingual and document-heavy workloads
Gemma
Open weight · Google
Routing: Classification, routing, and guard steps
Mistral
Open weight
Routing: Latency-critical short completions
Claude
Frontier · Anthropic
Routing: Long-document analysis and drafting
GPT
Frontier · OpenAI
Routing: General reasoning and complex tool use
Gemini
Frontier · Google
Routing: Very large context and multimodal inputs
What we build vs. what we integrate
Proprietary to AgentLabz
- Agent runtime and deterministic state machine
- Multi-agent orchestrator and task graph planner
- Memory, retrieval, and citation layer
- Evaluation harness and regression scoring
- Guardrail and policy engine
- Inference scheduler, batching, and cache reuse
Integrated, not rebuilt
- Frontier and open-weight foundation models
- GPU infrastructure and accelerated runtimes
- Vector and relational data stores
- Telephony, CRM, helpdesk, and collaboration APIs
- Observability and log pipelines
- Identity and SSO providers
What we are building next
Dated commitments are made contractually with enterprise customers; this is the engineering sequence.
Browser Agents
Agents that navigate authenticated web apps with recorded, replayable sessions.
Computer Use
Controlled desktop actions for legacy systems without APIs, under approval gates.
Vision Agents
Document, screenshot, and diagram understanding inside the agent loop.
Video Understanding
Long-form video ingestion for training, QA, and support workflows.
Marketplace
Publish, install, and version community and partner agent templates.
Fine-tuning
Managed LoRA fine-tunes on approved customer data, evaluated before rollout.
On-prem Deployment
Air-gapped installs on customer-owned GPU infrastructure.
Agent Collaboration
Cross-workspace agent teams with negotiated shared objectives.