AI Agentic Systems and Data
Systems that think, decide and act on your data.
Agents, knowledge and data engineered into one accountable operating layer. From retrieval and memory to orchestration, evaluation and governance, we build AI that leaves the chat window and does the work.
AI becomes valuable when it leaves the chat window and enters the operation. We engineer the whole intelligence loop, on data you can trust.
Ten layers. One loop.
AI that does real work is a system, not a model. We engineer every layer of it, and every layer strengthens the next: from the interface people trust down to the economics that let it scale.
- DesignWe design AI people can understand, direct and trust.
- KnowledgeWe turn fragmented information into retrievable operating context.
- IntelligenceWe engineer the intelligence layer around quality, speed, control and cost.
- MemoryWe give AI the continuity to understand the user, the work and the moment.
- AgencyWe build the runtime that turns model capability into dependable execution.
- OrchestrationWe orchestrate specialised agents around a shared outcome and accountable handoffs.
- ActionWe connect agents to the software, data and actions that businesses already depend on.
- EvaluationWe make AI quality visible from development through production.
- GovernanceWe engineer control, accountability and protection into every layer.
- ScaleWe optimise the infrastructure so intelligence stays fast, resilient and commercially viable.
Agents are only as good as their data.
Ingest
Every source, one pipeline. Documents, tables, events and logs arrive from wherever they live. Batch and streaming pipelines land them in a lakehouse or warehouse with lineage from the first byte.
Clean and chunk
Quality before quantity. We parse, deduplicate and validate against data contracts, redact what should never reach a model, then chunk with the structure of the source preserved.
Embed
Meaning becomes geometry. Chunks are embedded into a vector space where related ideas sit together, so hybrid search and reranking find the passage that answers the question, not just the keyword.
Link
Facts become a graph. Entities and relationships are extracted into a knowledge graph, so an agent can reason across customers, contracts and orders instead of reading isolated snippets.
Assemble
The right context, at the right moment. At run time we assemble a permission-aware context window, inside a token budget, and hand it to the agent with citations. Governed data in, accountable action out.
Autonomy is a setting.
Not a leap of faith. We design the control model first, then raise autonomy one notch at a time, as evaluations earn the trust.
Assist
AI answers. People act. Research, drafting and search across company knowledge.
Copilot
AI prepares. People approve. Support, sales, finance and operations workflows.
Agent
AI acts. People handle exceptions. Repeatable multi-step work: triage, reconciliation, resolution.
Multi-agent system
Agents coordinate. People set policy. End-to-end processes that span teams and systems.
Measured, not hoped.
Every agent ships with an eval suite, tracing and production monitors. Quality, latency and cost per task are numbers on a dashboard, not opinions in a meeting.
We trace every step an agent takes, score runs against golden datasets, and gate releases on regressions. The same telemetry watches production for drift in quality, latency and cost.
What we engineer.
Six capabilities, one accountable system.
- Agent runtimesHarnesses that turn a model into a dependable worker: planning, state, checkpoints, retries and recovery, with typed tools and hard budgets on steps and spend.
- Multi-agent orchestrationSupervisors, specialists and handoffs designed around one shared outcome. Parallel where it pays, sequential where accountability matters.
- RAG and knowledge graphsRetrieval that finds the right fact: structured chunking, hybrid search, reranking, entity graphs and permission-aware context assembly.
- Data platforms and pipelinesThe foundation under every agent: batch and streaming pipelines, lakehouse and warehouse models, data contracts and quality monitoring.
- Evaluation and observabilityEval suites built from your real tasks, tracing on every run and production monitors for quality, latency and cost per task.
- Guardrails and governanceLeast-privilege tools, sandboxed execution, prompt-injection defence, data protection and an audit trail for every decision an agent makes.
Model-agnostic. Stack-fluent.
Representative technologies across the ten layers, not a fixed stack. We choose per problem, on quality, control and cost.
AI SDK UI, CopilotKit, assistant-ui, AG-UI Protocol, OpenAI ChatKit, Gradio, Streamlit, PostgreSQL, Pinecone, Qdrant, Neo4j, Weaviate, Unstructured, Apache Kafka, Elasticsearch, OpenAI API, Anthropic Claude API, Google Gemini API, Hugging Face Transformers, vLLM, NVIDIA TensorRT-LLM, LiteLLM, Unsloth, Mem0, Zep, Letta, LangMem, Cognee, Graphiti, Redis, Supermemory, LangGraph, OpenAI Agents SDK, Google Agent Development Kit, Pydantic AI, Mastra, Strands Agents SDK, Temporal, Restate, Microsoft AutoGen, CrewAI, Semantic Kernel, LlamaIndex AgentWorkflow, Agent2Agent (A2A), NVIDIA NeMo Agent Toolkit, Agno, CAMEL-AI, Model Context Protocol, Composio, Pipedream Connect, Browserbase, Playwright, Apify, Zapier MCP, Arcade, Langfuse, Arize Phoenix, LangSmith, W&B Weave, Braintrust, TruLens, Ragas, OpenTelemetry, NVIDIA NeMo Guardrails, Guardrails AI, Lakera, Microsoft PyRIT, Presidio, Open Policy Agent, HashiCorp Vault, Amazon Bedrock Guardrails, Docker, Kubernetes, KServe, NVIDIA Triton, Ray Serve, BentoML, SkyPilot, Vercel AI Gateway
Questions, answered.
What is an agentic AI system?
An agentic AI system is software in which AI models do more than answer questions: they plan a task, retrieve the context they need, call tools and APIs to act, check their own work and hand off to people or other agents. Asymmetri engineers these systems end to end, from data and knowledge to the agent runtime, orchestration, evaluation and governance, so the AI completes real work inside your operation instead of living in a chat window.
How do you evaluate and monitor AI agents?
We build an evaluation suite from your real tasks before an agent ships, then run it as a regression gate on every change to prompts, models, tools or data. In production every run is traced, and we monitor task success, groundedness, latency and cost per task, with alerts when quality drifts. Tools such as Langfuse, LangSmith, Arize Phoenix, Braintrust and OpenTelemetry are representative of what we use.
Are you tied to one model provider?
No. Asymmetri is model-agnostic. We choose models per task on quality, speed, control and cost, and often route between several, including OpenAI, Anthropic Claude, Google Gemini and open-weight models served with tools like vLLM. Routing and evaluation are built in, so you can change models as the market moves without rebuilding the system.
How do you keep AI agents safe and governed?
Governance is designed into the architecture. Agents get least-privilege access to tools and data, risky actions run in sandboxes or wait for human approval, inputs are screened for prompt injection, personal data is detected and redacted, and every decision is logged for audit. Autonomy is raised one step at a time, from assist to copilot to agent, only as evaluations earn that trust.
How do we start an AI agent project with Asymmetri?
Bring us the hard problem. We start by framing the valuable decision or workflow, then design the interaction and control model, engineer the agent and its data foundation, prove performance, safety and economics with evaluations, and operate it in the real workflow. Write to hey@asymmetri.in to begin.
Bring us the hard problem.
We'll find the asymmetric answer.