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Agentic AI

Beyond chatbots.
Truly autonomous AI.

We build LLM-powered agents that go beyond question-answering — capable of understanding context, taking real actions via APIs, reasoning through complex workflows, and adapting over time. Purpose-built for enterprise.

What we deliver

Multi-agent OrchestrationLangGraph, AutoGen, CrewAI — stateful, collaborative agents
RAG & Knowledge IntelligenceVector databases, semantic search, document pipelines
Enterprise System IntegrationAPI tool use, MCP servers, real actions not just suggestions
Compliance & Policy AutomationIntelligent rule enforcement with full audit trails
MLOps & AI InfrastructureBedrock, Vertex AI, Azure OpenAI — production AI infra
80%Reduction in manual workflow steps with AI agents
10xFaster document processing vs manual review
24/7Autonomous agent availability with no human dependency
60%Cost reduction compared to equivalent human review processes
The problem

Most enterprise AI is
still just a chatbot.

The first wave of enterprise AI produced dashboards, recommendations, and conversational interfaces. Useful — but fundamentally passive. They answer questions. They don't take action.

Agentic AI is the next frontier. Agents that understand context across long workflows, connect to real systems via APIs, make multi-step decisions, delegate to specialist sub-agents, and adapt their approach based on outcomes — without human hand-holding at every step.

Sminetech builds production-grade agentic systems for regulated enterprises — with appropriate guardrails, audit trails, compliance controls, and the engineering rigour to make autonomous AI trustworthy in business-critical environments.

Where Agentic AI delivers the most value

1
High-volume document processingContracts, applications, reports — processed, extracted, and routed automatically
2
Complex approval workflowsMulti-step decisions that require policy reasoning and cross-system actions
3
Customer-facing intelligent automationBeyond scripted bots — agents that handle nuanced requests and escalate intelligently
4
Compliance & regulatory monitoringContinuous policy enforcement against real-time data streams
5
Knowledge managementOrganisational knowledge made searchable, retrievable, and actionable via RAG
Core capabilities

What we build in Agentic AI

From intelligent document processing to fully autonomous multi-agent systems — purpose-built for enterprise environments.

Multi-agent Orchestration
We design and build systems of specialised agents that collaborate — each with a defined role, tools, and decision scope — coordinated by an orchestrator that manages the overall workflow.
  • LangGraph stateful agent graphs with conditional routing
  • AutoGen multi-agent conversation frameworks
  • CrewAI role-based agent collaboration systems
  • Agent memory management across long-running workflows
  • Graceful degradation and human-in-the-loop escalation
RAG & Knowledge Intelligence
Retrieval-Augmented Generation systems that give AI agents access to your organisation's proprietary knowledge — with semantic search, context management, and citation tracking.
  • Vector database design with Pinecone, Weaviate, pgvector
  • Document ingestion, chunking, and embedding pipelines
  • Semantic search with re-ranking and relevance tuning
  • Hybrid search combining vector and keyword approaches
  • Citation tracking and source attribution for audit trails
Enterprise System Integration
Agents that connect to your real systems — CRMs, ERPs, databases, APIs — and take real actions, not just provide suggestions.
  • Tool definition and API integration for agent action-taking
  • MCP (Model Context Protocol) server implementations
  • Database query agents with natural language interfaces
  • Webhook and event-driven agent triggers
  • Secure credential management for agent API access
Compliance & Policy Automation
Intelligent systems that continuously monitor data, decisions, and workflows against regulatory rules — flagging violations, generating explanations, and triggering automated responses.
  • Policy encoding as LLM-interpretable rules
  • Regulatory document reasoning and Q&A systems
  • Automated compliance checking against transaction streams
  • Audit trail generation with reasoning explanations
  • Human oversight workflows for high-stakes decisions
Autonomous Process Execution
Agents that handle complete end-to-end processes without human involvement — from intake to completion — with appropriate checkpoints and escalation paths.
  • Process mapping and agentic workflow design
  • Multi-step autonomous task execution
  • Error handling, retry logic, and fallback strategies
  • Process monitoring and performance analytics
  • Gradual autonomy ramp-up with human oversight transition
MLOps & AI Infrastructure
The engineering infrastructure to deploy, monitor, and iterate on AI systems reliably — with the same rigour applied to production software.
  • Model serving with AWS Bedrock, Azure OpenAI, Vertex AI
  • LLM evaluation frameworks and quality benchmarking
  • Prompt version control and A/B testing
  • Agent observability with LangSmith and custom logging
  • Cost monitoring and token usage optimisation
Our methodology

How we build production-grade AI agents

A structured process from use-case discovery to production deployment — with reliability and governance built in.

01
Discover & Scope
Identify the highest-value agentic use cases in your business — where manual processes, decision bottlenecks, or knowledge gaps create the most friction.
  • Process audit and automation opportunity mapping
  • Data availability and quality assessment
  • Feasibility scoring (complexity vs value)
  • Compliance and risk boundary definition
  • Stakeholder alignment on scope and success metrics
02
Design the Agent Architecture
Design the agent system — tools, memory, orchestration topology, escalation paths, and human oversight mechanisms.
  • Agent role definition and capability mapping
  • Tool and API integration design
  • Memory and context management strategy
  • Guardrail and safety constraint design
  • Audit trail and explainability requirements
03
Build & Integrate
Implement the agent system with your chosen LLMs, vector databases, and enterprise system integrations — with full test coverage and evaluation frameworks.
  • Agent implementation with LangGraph / AutoGen / CrewAI
  • Vector database setup and document pipeline build
  • Enterprise system API integration and tool definition
  • Evaluation harness build and baseline benchmarking
  • Prompt engineering and optimisation
04
Validate & Harden
Rigorous testing — edge cases, adversarial inputs, hallucination rates, latency, and cost — before any production exposure.
  • Red-teaming and adversarial testing
  • Hallucination rate benchmarking and mitigation
  • Latency and cost profiling
  • Human evaluation of representative task samples
  • Compliance review of agent decisions and outputs
05
Deploy & Monitor
Production deployment with observability, cost monitoring, and continuous improvement — treating AI agents with the same engineering discipline as any production service.
  • Staged rollout with progressive autonomy increase
  • LangSmith / custom observability integration
  • Cost and token usage monitoring dashboards
  • Regular model and prompt evaluation cycles
  • Continuous improvement based on production feedback
Technology stack

Our Agentic AI tech stack

Best-in-class frameworks, models, and infrastructure — evaluated and selected for enterprise production requirements.

Agent Orchestration
LangGraphStateful, cyclical agent graphs with conditional edges
AutoGenMicrosoft multi-agent conversation framework
CrewAIRole-based agent crews for collaborative tasks
LangChainLLM pipelines, tool use, memory management
LLM Models
GPT-4o / GPT-4OpenAI — reasoning, tool use, structured output
Claude 3.5 / Claude 3Anthropic — long context, instruction following
Mixtral / Llama 3Open-source — cost efficiency, on-prem deployment
AWS BedrockManaged foundation models on enterprise AWS infra
Vector & Memory
PineconeManaged vector database, metadata filtering
WeaviateHybrid search, multi-tenancy, modules
pgvectorVector search inside PostgreSQL
RedisShort-term agent memory and session caching
AI Infrastructure
AWS BedrockManaged models, RAG, agent actions on AWS
Azure OpenAIGPT models with Azure compliance and security
GCP Vertex AIPaLM, Gemini, managed ML pipelines
SupabaseBackend-as-a-service for agent persistence
Who this is for

Agentic AI use cases
by industry

BFSI & Lending
Loan application processing, document verification, compliance monitoring, fraud pattern analysis, and customer query handling at scale.
Fintech & Insurance
Insurance product recommendation, claims triage, KYC document processing, onboarding automation, and regulatory reporting.
Manufacturing
Supply chain exception handling, quality control document processing, maintenance request routing, and procurement workflow automation.
Pharma & Healthcare
Clinical trial document processing, regulatory submission preparation, adverse event monitoring, and medical literature synthesis.
Retail & E-commerce
Product catalogue management, customer support escalation, pricing intelligence, inventory exception alerts, and supplier communication.
Technology & SaaS
Support ticket triage and resolution, code review assistance, customer success automation, and internal knowledge management.
Frequently asked questions

Common questions about Agentic AI

RPA follows rigid, deterministic rules — it breaks when inputs vary. Agentic AI reasons about inputs, handles variation, makes judgment calls, and escalates when uncertain. It's better suited to unstructured data, natural language inputs, and processes that require contextual decision-making rather than rule execution.
We design multiple safeguards: structured output schemas that constrain LLM responses, retrieval-augmented generation to ground answers in verified sources, confidence scoring and uncertainty detection, human-in-the-loop checkpoints for high-stakes decisions, and comprehensive evaluation harnesses that benchmark hallucination rates before production deployment.
Yes. For organisations with data sovereignty or compliance requirements, we build agents using open-source models (Llama, Mixtral) deployed on your infrastructure, or configure AWS Bedrock / Azure OpenAI with data residency and no-training commitments. We design for your compliance constraints from the start.
A focused single-agent deployment for a well-defined use case can be production-ready in 6–10 weeks. Multi-agent orchestration systems for complex enterprise processes typically take 12–20 weeks including evaluation, hardening, and integration testing. We recommend starting with a scoped proof of concept before committing to a full build.
We build audit trails into every agent — logging inputs, reasoning steps, tool calls, outputs, and any human overrides. For regulated industries, we design explainability mechanisms that can justify agent decisions in plain language, and we implement human approval workflows for decisions above configurable risk thresholds.
Related services

Often combined with Agentic AI

Cloud & DevOps
The cloud infrastructure and MLOps foundation that AI agents are deployed and operated on.
Learn more →
Product Engineering
Embedding AI capabilities into enterprise applications and platforms.
Learn more →
Managed Services
Ongoing monitoring, maintenance, and optimisation of production AI agent systems.
Learn more →
Ready to get started with Agentic AI? Start with a free 2-week assessment — no commitment, no obligation.
Interested in Agentic AI? Get a free assessment — no commitment, no sales pitch.