Agentic AI Solutions
Production-Grade Autonomous AI Systems
Multi-agent orchestration, tool-calling architectures, and human-in-the-loop safety - built for enterprise-scale autonomy.
Why This Matters
Enterprise AI Is Moving From Prompts to Agents.
The era of single-prompt LLM calls is over. In 2026, the most impactful AI systems are agentic - they plan, reason, use tools, and execute multi-step workflows autonomously. From automating complex research tasks to orchestrating cross-system business processes, agents represent the next frontier of enterprise AI.
But production agents are fundamentally different from demo agents. They need deterministic fallback paths, human approval gates for high-risk actions, persistent memory across sessions, and robust observability. Without these, agents hallucinate, loop infinitely, or take unauthorized actions.
We build production-grade agentic systems using battle-tested frameworks like LangGraph for stateful graph execution, CrewAI for role-based multi-agent collaboration, and Microsoft AutoGen for complex conversational agent networks - all with enterprise safety guardrails baked in from day one.
Our Tech Stack
Production-Grade Tools We Deploy
Agent Frameworks
Orchestration & Chaining
LLM Providers
Tool Integration
Memory & State
Observability & Monitoring
Guardrails & Safety
Infrastructure
Architecture Deep-Dive
How We Build It
Multi-Agent Orchestration
Building supervisor-worker agent topologies with LangGraph's stateful graph execution engine. Cyclic workflows, conditional branching, and human-in-the-loop approval gates for complex enterprise processes.
- ReAct, Plan-and-Execute, and Reflexion agent patterns for different use cases
- Supervisor agents that delegate tasks to specialized worker agents
- LangGraph's StateGraph for deterministic workflow execution with cycles
- Conditional edges and branching logic based on agent outputs
- Human-in-the-loop approval gates for high-risk or high-cost actions
- Parallel agent execution with fan-out/fan-in patterns
Tool Use & API Integration
Agents that autonomously call 50+ enterprise APIs using OpenAI function calling, Anthropic tool use, and the Model Context Protocol (MCP) - with schema validation, retry logic, and sandboxed execution.
- OpenAI function calling with strict JSON schema validation
- Anthropic tool use with structured input/output definitions
- MCP servers for connecting agents to databases, APIs, and file systems
- Rate limiting, retry logic, and circuit breakers for API reliability
- Sandboxed execution environments for untrusted tool outputs
- Dynamic tool discovery - agents choose tools based on context
Memory & Context Management
Short-term conversation memory with Redis, long-term episodic memory with vector stores, and persistent agent state via LangGraph checkpointers - so agents remember context across sessions.
- Redis-backed sliding window memory for conversation context
- Vector store-based long-term episodic memory (Pinecone, Qdrant)
- LangGraph checkpoint persistence for resumable workflows
- Mem0 for personalized, cross-session agent memory
- Context window optimization with summarization chains
- Memory-aware retrieval for agents with 100K+ interaction histories
Safety & Human-in-the-Loop
NVIDIA NeMo Guardrails for content safety, configurable approval workflows for high-risk actions, full audit trails with LangSmith, and deterministic fallback paths when agents encounter edge cases.
- NeMo Guardrails for topic steering, content safety, and jailbreak prevention
- Configurable approval workflows - agents pause and request human approval
- Full audit trails - every agent action logged with LangSmith traces
- Deterministic fallback paths when confidence drops below threshold
- Output validation against business rules before execution
- Cost controls - per-agent budget limits and token consumption alerts
Data Security, Governance & Safety
Enterprise AI demands enterprise-grade security. Every solution we deploy follows strict data sovereignty, safety, and compliance standards.
Data Sovereignty
- Your data stays in your infrastructure - always
- Deploy on your cloud (AWS, Azure, GCP) or on-premise
- No data leaves your environment
- Full compliance with regional data residency requirements
Model Safety & Guardrails
- NVIDIA NeMo Guardrails for content safety
- PII detection and redaction with Presidio
- Prompt injection defense and input sanitization
- Hallucination detection and factual grounding
Access Control & Audit
- Role-based access control for all AI systems
- Immutable audit logs for every interaction
- SOC 2 Type II, ISO 27001 compliance frameworks
- GDPR, HIPAA, and industry-specific regulations
Responsible AI
- Bias testing with Fairlearn and AI Fairness 360
- Model explainability via SHAP and LIME
- Transparency reports for stakeholders
- Continuous fairness monitoring in production
FAQ
Frequently Asked Questions
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