AI Agent Development Services
Production-grade AI agents that plan, act, and self-correct, deployed into your stack in 30 days. Starting at $5,000.
Free. 30 minutes. No prep required.
- Dhoni: RIVERBORN'S OWN PRODUCTION AGENTIC SYSTEM
- 100K+ WORLDWIDE USERS SERVED
- GLOBAL DELIVERY FROM BANGLADESH
AI agent development is the practice of designing, building, and deploying autonomous LLM-powered systems that plan, execute, and self-correct multi-step business tasks. For your team, that replaces brittle RPA scripts and overflowing ticket queues. You get agents that reason across tools, pull grounded data, and escalate to humans only when they should. Riverborn is an AI agent development company that ships these systems, along with multi-agent systems and generative AI products, using LangChain, CrewAI, LangGraph, AutoGen, LlamaIndex, Google ADK, and other agent SDKs and orchestration tools. We run the same stack across our own 10+ live AI products, including Dhoni (the enterprise evolution of Vocalo.ai), with 100K+ live voice interactions in production.
AI agent development sits within applied artificial intelligence engineering. Agent types include conversational agents, task-execution agents, retrieval-augmented agents, and multi-agent systems. Each agent is built from four components: a perception module, a reasoning engine, a tool-calling interface, and a memory system. Riverborn delivers agentic AI development services calibrated to your integration surface, compliance posture, and evaluation thresholds, whether you need a single agent or a coordinated network. We operate as an agentic AI development company and AI development agency for CTOs, VPs of Engineering, and Heads of AI at Series A to C companies and global enterprises.
What Your Team Gets
on Google ADK, CrewAI, LangGraph, and AutoGen, so your team coordinates specialist agents instead of maintaining wrapper logic.
the Dhoni production stack.
via Qdrant, Pinecone, Weaviate, or pgvector retrieval.
on GPT-5.6, Claude Sonnet 5, Claude Fable 5, and Claude Opus, so downstream systems consume your agent output without parsing logic.
CRM, ERP, and HRIS via REST API, OAuth 2.0, and webhook callbacks.
a production-ready agent in 4 weeks, from $5,000.
What We Build: AI Agent Types
Six agent architectures, matched to the shape of your operation, not the other way around. Riverborn delivers custom AI agent development services across each type.
Conversational AI Agents
Handle multi-turn support, knowledge retrieval, and employee assistance with memory that persists across sessions. Your users don't have to repeat themselves across the conversation.
→ See also: AI Chatbot DevelopmentTask-Execution Agents
Run defined business processes without human intervention at each step: data extraction, document summarization, compliance report generation, scheduled operational workflows. Your agent receives a task spec, executes an ordered sequence of tool calls, and returns structured output to the requesting system.
Retrieval-Augmented Agents
Ground outputs in your private data via a vector retrieval pipeline with reranked context injection. Your team gets answers backed by source documents, not hallucinated responses.
→ See also: NLP and RAG DevelopmentVoice Agents (Telephony)
Operate on real telephony with inbound/outbound calling, real-time STT, and TTS under sub-500ms response latency. This is the architecture behind Dhoni's 100K+ production interactions.
Multi-Agent Systems
Coordinate specialist agents through a supervisor. CrewAI handles role-based delegation. LangGraph manages stateful graphs with conditional branching. AutoGen manages conversational multi-agent loops.
→ See also: Agentic AI Systems DevelopmentDomain-Specific Enterprise Agents
Pre-architected agents for customer support and CX, sales, HR, finance, and IT. Each agent ships with pre-mapped tool integrations, domain-aligned prompts, and role-specific human escalation paths.
Our Technical Approach
Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls. That’s the layer that separates builders from resellers, and it’s the layer most agent vendors skip because it doesn’t demo well.
Riverborn designs every agent on a five-layer architecture: perception, reasoning, planning, execution, evaluation. Each layer is documented so your engineering team owns it post-handover. These agentic AI software development services are engineered for AI agent consulting engagements where governance and evaluation are first-class, not afterthoughts.
Agent Architecture Layers
1. Perception.
Ingests inputs from REST APIs, databases, file systems, and telephony streams.
2. Reasoning.
Routes through a selected LLM: GPT-5.6, Claude Sonnet 5, Claude Fable 5, and Claude Opus, or Llama 4. Structured output and chain-of-thought prompting are applied at this layer.
3. Planning.
Decomposes objectives into ordered subtasks using ReAct or a task-planning module inside CrewAI or LangGraph.
4. Execution.
Dispatches function calls to external tools. Each tool is defined as a JSON schema with documented fallback paths. Agents call tools via OpenAI's function-calling specification or Anthropic's tool-use API.
5. Evaluation.
Runs automated checks via RAGAS before results ship downstream, covering three scores: faithfulness, answer relevance, and context precision.
Memory Architecture
Memory splits by purpose. A short-term buffer holds in-session context, up to 128K tokens on GPT-5.6. A long-term vector store handles semantic recall across sessions, so your agent remembers prior interactions without re-ingesting full history on every call. You pick the vector database by deployment profile.
Qdrant
Vector database
Pinecone
Managed high-throughput
Weaviate
Self-hosted clusters
pgvector
Air-gapped / PostgreSQL
Multi-Agent Coordination & Guardrails
Multi-agent coordination uses role-based delegation. A supervisor agent decomposes the objective and assigns subtasks to specialists. It aggregates output and triggers self-correction on confidence-threshold failures. Input and output validation runs through Guardrails AI and LlamaGuard. This is the layer most LLM-wrapper builds omit entirely, and the layer your compliance team will check first.
Orchestration
Google ADK · CrewAI · LangGraph · AutoGen
Validation
Guardrails AI · LlamaGuard
Industries & Use Cases
Riverborn has shipped AI agent solutions across regulated and high-throughput verticals. The patterns below are active deployment architectures. Each cell links to a fuller industry solution page.
Healthcare & Life Sciences
Clinical scheduling agents integrated with EHRs via REST API, compressing administrative coordination time on common scheduling patterns.
→ AI for healthcareFinancial Services & Fintech
Compliance monitoring agents with audit trail, rule-based classification, and regulator-ready escalation paths across CRM, trading, and archive systems.
→ AI for financial servicesE-commerce & Retail
Multi-agent order management coordinating inventory, fulfillment, and customer service through a shared message bus, each agent autonomous within its domain.
→ AI for e-commerce and retailSaaS & Technology
Internal-ops agents for customer success, churn signals, and engineering triage, built on the same agent stack Riverborn runs in production on its own 10+ products.
→ AI for SaaS and technology companiesManufacturing & Supply Chain
Operations coordination agents monitoring supplier signals, defect patterns, and scheduling, routing exceptions to the right floor manager with evidence attached.
→ AI for manufacturing and supply chainReal Estate & PropTech
Lead qualification, tenant service, and transaction coordination agents integrated with MLS and CRM systems.
→ AI for real estate and PropTechTechnology Stack
Every agent ships on the production-validated stack below.
| Category | Technologies & Frameworks |
|---|---|
LLM Models | GPT-5.6Claude Sonnet 5Claude Fable 5Claude OpusLlama 4 |
Orchestration Frameworks | Google ADKLangChainCrewAILangGraphAutoGenLlamaIndex |
Vector Databases | QdrantPinecone (managed)Weaviate (self-hosted)pgvector (PostgreSQL) |
Cloud Infrastructure | AWS Lambda + API GatewayGCP Cloud RunKubernetes auto-scalingGCP Compute Engine |
Voice / Telephony | TwilioCustom SIPReal-time STT/TTS pipeline |
Enterprise Connectors | SalesforceSAPTwentyWorkdayServiceNowHubSpot (REST API + OAuth 2.0) |
Evaluation | RAGAS: faithfulnessAnswer relevanceContext precision |
Guardrails | Guardrails AILlamaGuardJSON schema validation |
LLM Models
Orchestration Frameworks
Vector Databases
Cloud Infrastructure
Voice / Telephony
Enterprise Connectors
Evaluation
Guardrails
How It Works: Our Development Process
Riverborn ships agents through a five-step process. Standard engagements complete in 7 to 12 weeks. The fixed-scope 30-Day AI Agent MVP Build delivers a production-grade agent on a predictable timeline, starting at $5,000.
Discovery & Architecture
We map your business objective to specific agent opportunities, then design the full system architecture.
Deliverable
Data & Integration Design
We map your data sources, specify the RAG pipeline where applicable, and document API integration for every ERP, CRM, and HRIS system in scope.
Deliverable
Agent Build & Engineering
Our engineers build core agent logic, tool integrations, memory, guardrails, and multi-agent orchestration. You get weekly build demos so nothing drifts off-spec.
Deliverable
Testing & Governance
RAGAS-based evaluations run alongside edge-case testing and human-in-the-loop escalation design. Your team signs off on governance before production.
Deliverable
Deployment & Monitoring
We deploy to AWS Lambda or GCP Cloud Run with Kubernetes auto-scaling, and benchmark latency against our sub-200ms p95 latency SLA.
Deliverable
Why Riverborn
Precedence Research values the global AI agents market at $7.92 billion in 2025, projected to reach $236.03 billion by 2034. Most of that spend will route to vendors who can actually ship in production. You can hire a builder or a reseller. Riverborn builds, and runs the same stack on its own products that it ships to clients.
PRODUCTION PROOF
Production proof, not portfolio claims.
Riverborn's agent framework powers Dhoni (the enterprise evolution of Vocalo.ai), which has processed 100K+ live voice interactions in production. Few vendors in this category operate an AI product portfolio at this scale.
HUMAN-JUDGMENT ARCHITECTURE
Human-judgment architecture by default.
MIT Sloan Management Review and BCG's November 2025 research on agentic AI adopters found that 79% of extensive adopters are investing in AI that generates insights for a human decision maker, rather than fully autonomous decision-and-implementation scenarios. That is the pattern Riverborn builds to by default.
MULTI-AGENT ORCHESTRATION
Multi-agent orchestration as a named deliverable.
We design and ship multi-agent architectures on CrewAI, LangGraph, and AutoGen. Most vendors in this category do not name specific orchestration frameworks in their methodology. MAS is a standard option for you, not a premium add-on.
FIXED-SCOPE PATH
30-day fixed-scope path.
The 30-Day AI Agent MVP Build delivers a production-grade agent on a defined timeline. You get scope certainty without the budget drift of open-ended engagements. Projects start at $5,000.
COST STRUCTURE
A cost structure that pays for the rigor.
Our Bangladesh delivery model gives you a 40 to 60% cost reduction vs US and EU agencies at identical production-grade benchmarks. Structural advantage, not a discount on quality. Engagements calibrate to buyer stage. Startups validate first products with the 30-Day MVP. Series A to C teams scale into multi-agent architectures. Enterprise teams embed agents into existing SAP, Salesforce, and ServiceNow environments. If you’re evaluating AI agent development company options, start with an AI readiness audit to validate fit before you commit to a full build.
4.8+ avg.
product rating
10+ live
AI products shipped
100K+
global users served
Sub-200ms
p95 latency SLA
HIPAA + PCI-DSS
compliant deployment patterns
RAGAS
eval suite on every build
Related Services
Agentic AI Systems Development
For fully autonomous multi-agent architectures at enterprise scale with persistent state and complex agent-to-agent coordination.
Learn more →AI Chatbot Development
For structured conversational AI without full task-autonomy.
Learn more →AI Consulting and Strategy
If you are confirming whether agents fit your problem before you commit to a build.
Learn more →Frequently Asked Questions
AI agent development is the practice of designing, building, and deploying autonomous LLM-powered systems that execute multi-step tasks without human input at each step. It involves tool-calling APIs, multi-step reasoning chains, persistent vector memory, and self-correction loops. The agent does the work, not just the response.
Riverborn projects start at $5,000. Final cost depends on agent complexity, tool integrations in scope, whether multi-agent orchestration is required, and your target deployment infrastructure. The 30-Day AI Agent MVP Build is the fixed-scope entry point. Our Bangladesh delivery model keeps costs 40 to 60% below comparable US and EU agencies.
Standard engagements run 7 to 12 weeks across discovery, integration design, build, testing, and deployment. The 30-Day AI Agent MVP Build is a fixed-scope version delivering a production-ready agent system inside a 30-day window. It's sized for startups and Series A to C teams with a defined use case.
A chatbot responds to user inputs using a single LLM call or predefined logic. An AI agent autonomously executes multi-step tasks, invokes external tools via function-calling APIs, maintains persistent vector memory, and self-corrects on output failures. For standalone conversational layers, see Riverborn's AI chatbot development service.
Riverborn builds on LangChain for chain-based orchestration, CrewAI for role-based multi-agent delegation, LangGraph for stateful agent graphs with conditional branching, AutoGen for conversational multi-agent loops, and LlamaIndex for retrieval-augmented pipelines. Framework selection depends on coordination requirements, state complexity, and your integration scope.
Yes. Riverborn integrates agents with Salesforce, SAP, Workday, ServiceNow, HubSpot, and custom ERP and CRM systems via REST API and OAuth 2.0. Integration is scoped during the Data & Integration Design phase before engineering begins. For broader integration scopes, see AI integration services.
A multi-agent system is an architecture where two or more AI agents operate as coordinated specialists under an orchestration layer. A supervisor agent decomposes objectives, delegates subtasks to specialists, aggregates returned output, and triggers self-correction on failures. Full enterprise MAS coverage is at agentic AI systems development.
Yes. The 30-Day AI Agent MVP Build delivers a production-grade AI agent system in 30 days with a fixed scope, defined deliverables, and a documented deployment process. Starting at $5,000, it fits startups and Series A to C companies with a defined use case needing a predictable timeline.