sea view lab

Agentic.Now.

Supporting startups and organizations in building intelligent digital workforces — from autonomous AI agents to full-stack systems.

Enterprise-grade AI agents require sophisticated engineering—from architecture to production

Agent Architecture & Design Patterns

Building scalable multi-agent systems with proper separation of concerns, event-driven communication, and modular components. Implementing ReAct, Chain-of-Thought, and hierarchical agent patterns for complex reasoning workflows.

Guardrails & Safety Systems

Implementing comprehensive safety measures including content filtering, output validation, and behavioral constraints. Building monitoring systems to detect hallucinations, prompt injections, and unexpected agent behaviors.

Advanced Prompt Engineering

Crafting sophisticated prompts with few-shot learning, chain-of-thought reasoning, and dynamic context management. Implementing prompt versioning, A/B testing, and automated prompt optimization for consistent performance.

Observability & Performance Monitoring

Building comprehensive monitoring systems with distributed tracing, metrics collection, and real-time alerting. Tracking agent performance, token usage, latency, and success rates across complex multi-agent workflows.

Cost Optimization & Resource Management

Implementing intelligent caching strategies, model selection algorithms, and resource pooling to minimize operational costs. Building cost attribution systems and automated scaling based on demand patterns and budget constraints.

Model Context Protocol (MCP)

Implementing MCP to enable AI agents to securely connect with external systems, databases, and APIs through standardized interfaces. Building robust tool calling mechanisms, resource access patterns, and context sharing protocols that work across different AI models and platforms for seamless integration.

Knowledge Graphs

Leveraging sophisticated graph databases and algorithms to build intelligent knowledge systems that understand complex relationships and dependencies. AI agents are empowered with contextual reasoning capabilities through interconnected data structures that enable semantic understanding and intelligent decision-making.

Vector Databases & RAG Systems

Designing and implementing high-performance vector storage solutions with semantic search capabilities. Building sophisticated Retrieval-Augmented Generation systems that combine real-time data retrieval with contextual AI reasoning for accurate, up-to-date responses.

How we help you build production-ready AI agents

Agent Architecture Review

Comprehensive evaluation of your AI agent systems including architecture patterns, scalability design, inter-agent communication protocols, and integration strategies. We assess performance bottlenecks, identify optimization opportunities, and provide actionable recommendations for production-ready deployments.

Multi-Agent Coordination

Design and implementation of sophisticated agent coordination systems with proper task distribution, state management, and communication protocols. We build robust systems that can handle complex multi-step processes, dynamic routing, and fault-tolerant execution patterns across distributed agent networks.

Safety & Guardrails Implementation

Development of comprehensive safety systems including content filtering, output validation, behavioral constraints, and monitoring for prompt injections. We implement multi-layered security measures to ensure your agents operate within defined boundaries and maintain consistent, safe behavior in production environments.

Observability & Cost Optimization

Implementation of advanced monitoring, logging, and alerting systems with real-time performance tracking and cost attribution. We build dashboards for agent performance metrics, implement intelligent caching strategies, and optimize resource utilization to minimize operational costs while maintaining high availability.

Connect

Ready to dive into the Agentic Era?
Let's discuss your agentic AI architecture and implementation strategy.