Production AI Agents in Weeks
Not prototypes. Not demos. Production-grade autonomous agents deployed across 5 leading frameworks — with enterprise governance, observability, and human-in-the-loop controls from day one.
Agentic AI accelerators for enterprise deployment
Agentic AI accelerators are pre-built orchestration frameworks, connector libraries, and governance templates that eliminate 80% of the boilerplate code required to deploy production AI agents. They provide ready-made patterns for tool integration, memory management, and multi-step reasoning across 5 major frameworks.
Our Agentic AI Accelerator Platform includes 100+ enterprise connectors, production-tested governance frameworks, and pre-configured orchestration patterns — letting your team focus on agent logic instead of infrastructure scaffolding.
Support for Microsoft Agent Framework, Salesforce AgentForce, LangGraph, AWS Bedrock Agents, and Java Spring AI means you're never locked into a single vendor. Deploy multi-agent systems that span frameworks while maintaining unified observability.
Production agents across every platform
Choose your framework. We provide the accelerators, governance, and production infrastructure.
Microsoft Agent Framework
Build autonomous agents on Azure with Semantic Kernel orchestration, Microsoft Graph connectors, and Copilot Studio integration. Enterprise SSO and compliance built in.
Salesforce AgentForce
Deploy CRM-native agents that autonomously handle sales outreach, service resolution, and commerce workflows. Direct Data Cloud and MuleSoft connectivity.
LangGraph Orchestration
Complex multi-step agent workflows with stateful graph execution, conditional branching, and persistent memory. Ideal for research, analysis, and decision-support agents.
AWS Bedrock Agents
Production agents on AWS with Bedrock orchestration, Lambda tool execution, and native IAM security. Multi-agent workflows with built-in guardrails and Knowledge Base integration.
Java Spring AI
Enterprise Java teams get first-class agent support with Spring Boot integration, familiar dependency injection patterns, and battle-tested JVM performance at scale.
Agent Governance
Unified guardrails across all frameworks — token budgets, action approvals, audit trails, PII filtering, and rollback capabilities. SOC 2 and ISO 27001 compliant.
From concept to production agents
A proven 5-phase methodology that delivers autonomous agents in weeks.
Agent Discovery
We identify high-value automation opportunities, map workflows to agent patterns, and select the optimal framework based on your existing tech stack and governance requirements.
Accelerator Configuration
Pre-built orchestration templates, connector libraries, and governance policies are configured for your environment. 80% of scaffolding is eliminated before your team writes a line of code.
Agent Development
Your team (or our FDEs) builds agent logic on top of the accelerator platform. Tool integrations, prompt chains, and decision graphs are assembled using tested patterns.
Governance & Testing
Automated red-teaming, boundary testing, and compliance validation ensure agents behave predictably. Human-in-the-loop approval workflows are configured for high-risk actions.
Production Deployment
Agents are deployed with full observability — token usage, decision traces, error rates, and business KPIs. Auto-scaling and failover ensure enterprise-grade reliability.
Platform Metrics
Frequently asked questions
Agentic AI accelerators are pre-built orchestration frameworks, connector libraries, and governance templates that eliminate 80% of the boilerplate code required to deploy production AI agents. They provide ready-made patterns for tool integration, memory management, and multi-step reasoning across 5 major frameworks including LangGraph, AWS Bedrock Agents, and Microsoft Agent Framework.
Using agentic AI accelerators, enterprises typically deploy production-grade AI agents in 4–6 weeks instead of 4–6 months. The accelerator platform eliminates scaffolding work by providing pre-built orchestration templates, 100+ enterprise connectors, and tested governance policies that are configured for your environment.
Maharadha supports 5 leading AI agent frameworks: Microsoft Agent Framework (Semantic Kernel), Salesforce AgentForce, LangGraph, AWS Bedrock Agents, and Java Spring AI. Multi-agent systems can span multiple frameworks while maintaining unified observability and governance.
Human-in-the-loop AI governance is a control framework where autonomous AI agents must obtain human approval before executing high-risk actions. It includes configurable approval workflows, token budgets, action audit trails, PII filtering, and automatic rollback capabilities to ensure agents behave predictably in enterprise environments.
Enterprise agentic AI development costs vary by scope, but accelerator platforms reduce total cost by 60–70% compared to building from scratch. By eliminating 80% of boilerplate code and providing pre-tested governance frameworks, organizations avoid months of infrastructure development and focus investment on agent logic and business outcomes.
Ready to deploy AI agents at scale?
Tell us your use case. We'll recommend the right framework, configure accelerators, and have agents in production within weeks.