luminovainfotech

AI

Agentic AI

Production-grade AI agents that act, not just chat

Agentic systems can plan, use tools, and make decisions across multi-step workflows. They are also significantly harder to ship than chatbots. We design, build, and operate agent systems with the orchestration, observability, and safety controls they need to run in production.

What we deliver

Agentic AI capabilities, end to end

Agent design and orchestration

We design agent loops that match the shape of the work — single-agent for narrow tasks, multi-agent or graph-structured for complex workflows.

  • Workflow decomposition and tool surface design
  • Single-agent, supervisor-and-workers, and graph-based orchestrations
  • Memory: short-term context, long-term knowledge, and episodic recall
  • Human-in-the-loop checkpoints for high-stakes steps

Tool use and integrations

Agents are only as useful as the tools they can call. We build robust, well-documented tool surfaces with type safety and proper error handling.

  • Custom tools wrapping internal APIs and data platforms
  • Model Context Protocol (MCP) servers for standardized integration
  • Structured outputs and constrained decoding for reliable parsing
  • Retry, timeout, and circuit-breaker patterns

Evaluation and observability

Agent traces are complex. We instrument every step so failures are debuggable, regressions are caught, and improvements are measurable.

  • End-to-end tracing across agent steps and tool calls
  • Trajectory-level evals: task success, cost, latency, safety
  • Replay and regression test suites
  • Cost and token-budget tracking per agent run

Safety and policy controls

Agents that take real actions need real safeguards. We design controls at every layer — model, runtime, and audit.

  • Action allow-lists and confirmation gates for destructive operations
  • Rate limiting and per-tenant budgets
  • Prompt injection defenses and input sanitization
  • Full audit logs of decisions and tool calls

How we work with you

Engagement shapes

Three typical ways we engage on agentic ai — adapted to your scope, timeline, and team.

3–4 weeks

Agent Feasibility Study

Spike a working agent, evaluate against a realistic task set, and report on viability and cost.

10–20 weeks

Production Agent Build

Full-stack agent system with orchestration, tools, evals, observability, and operations playbook.

Ongoing

Agent Run Partner

We operate your agents: monitor, debug, iterate prompts and tools, roll out model upgrades.

Tools & technologies

Built on what your teams already know

We work with industry-standard tooling and open standards — no proprietary lock-in.

Agent frameworks
Anthropic Claude Agent SDKLangGraphCrewAIAutoGenOpenAI Agents SDK
Protocols
Model Context Protocol (MCP)OpenAPI tool specsJSON schema
Observability
LangSmithBraintrustOpenTelemetrycustom trace stores

Let's talk

Tell us what you're building.

Share the shape of your initiative and we'll respond within one business day with a tailored point of view — and the names of the senior people who would lead the work.

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