--- title: Enterprise AI Agents That Work Across Your Systems | Avinya Labs description: Enterprise AI agents that read requests, gather what they need across CRM, ERP, ticketing and documents, and act, with a person approving anything high-stakes. Results from delivered engagements. url: /solutions/enterprise-ai-agents.html --- Solutions / Enterprise AI agents # AI agents that finish the work, and ask before anything that matters. Agents that read a request, gather what they need across your systems, and act on it. Low-risk steps run on their own. Payments, records and customer messages wait for a person. [Book a call](https://calendly.com/abbylester/30-mins-meeting) [See the results](/solutions/enterprise-ai-agents.html#outcomes) 1. Ticketing Read request #4821, refund over limit Done Working 2. CRM Read 1 customer account Done Working 3. ERP Matched 2 invoices Done Working 4. Knowledge Found the refund policy Done Working 5. Approval Refund is over the limit, asked Finance Approved Needs approval 6. Payments Released refund, logged to audit trail Done Working Illustrative run, sample data [~40%Less manual effort](/case-studies/enterprise-ai-agent-platform.html) [>80%Reduction in manual document review](/case-studies/enterprise-document-intelligence.html) [~50%Fewer manual handling cases](/case-studies/multi-agent-voice-ai.html) ## Why most automation stalls. Scripts, chatbots and copilots each stop one step short of the work. ### Rules break on messy inputs Scripts handle fixed forms well. Emails, PDFs and free-text requests need reading, not matching. ### Chatbots answer, then stop A chatbot can explain the refund policy. It cannot check the invoice and issue the refund. ### People become the integration Tickets, accounts, invoices and policies sit in separate tools, so someone copies between them all day. ### Full automation is a risk Software that moves money or changes records with no checkpoint is how mistakes reach customers. By hand 1. Read the request Person 2. Look up the account Person 3. Match the invoices Person 4. Check the refund policy Person 5. Release the refund Person 5 steps done by a person With an agent 1. Read the request Agent 2. Look up the account Agent 3. Match the invoices Agent 4. Check the refund policy Agent 5. Release the refund Approval 1 decision made by a person Illustrative, based on the sample request above ## Where our agents already work. Each row is a delivered engagement. Select one to read it. [**Business operations**Workflow agent Retrieves data across business systems, proposes actions, and routes high-stakes ones to a person.~40% less manual effort](/case-studies/enterprise-ai-agent-platform.html) [**Document-heavy operations**Document agent Extracts and classifies high-volume documents, sending only exceptions to people.>80% less manual review](/case-studies/enterprise-document-intelligence.html) [**Healthcare**Voice agent Handles scheduling, verification and follow-up calls, escalating to a person when needed.~50% fewer manual handling cases](/case-studies/multi-agent-voice-ai.html) [**Legal**Research agent Searches case law and filings, with every answer traced to its source.~50% faster review time](/case-studies/ai-legal-assistant.html) [**Insurance**Data platform Connects policy, claims and correspondence data into one workflow.~2.5x faster processing](/case-studies/ai-platform-insurance-broker.html) [**Travel**Support agent Answers booking and support questions from live inventory, around the clock.~35% fewer repeat contacts](/case-studies/ai-travel-chatbot.html) [**Field operations**Workflow automation Feeds AI extraction into a rules engine, so the same input gives the same output.~70% faster document prep](/case-studies/ai-workflow-automation.html) ## Controls built in, not bolted on. Each one is running in a system we delivered. ### Approval before high-stakes actions Payments, record changes and anything over a limit wait for a person before they execute. Proven in: AI agent platform → Link: /case-studies/enterprise-ai-agent-platform.html ### Escalation to a person When a conversation goes outside what the agent should handle, it hands over to a human operator. Proven in: Voice AI → Link: /case-studies/multi-agent-voice-ai.html ### Exception-only review Confident results go through. Low-confidence cases go to a review queue instead of being guessed. Proven in: Document intelligence → Link: /case-studies/enterprise-document-intelligence.html ### Answers you can trace Every answer links back to the document it came from, so reviewers check the source, not a summary. Proven in: Legal AI assistant → Link: /case-studies/ai-legal-assistant.html ### Role-based access and audit trail People see only what their role allows, and every record change is logged. Proven in: Insurance AI platform → Link: /case-studies/ai-platform-insurance-broker.html ### Rules where results must repeat AI reads the input; a deterministic rules engine produces the output, so it comes out the same every time. Proven in: Workflow automation → Link: /case-studies/ai-workflow-automation.html ## What we build agents with. Chosen per workflow. Models can change later without a rebuild. Generative AI and large language models - OpenAI - Anthropic Claude - Google Gemini - Meta Llama - Mistral AI - Cohere - Hugging Face - OpenRouter - LangChain - LlamaIndex AI agents and orchestration - LangGraph - AutoGen - CrewAI - Semantic Kernel - Agentic workflows - Tool calling - Function calling - Multi-agent systems Vector databases and semantic search - Pinecone - Weaviate - Milvus - Qdrant - Chroma - pgvector - Elasticsearch vector search AI integration and APIs - REST APIs - GraphQL - Webhooks - Microservices - API gateways - SDK integrations - Third-party AI APIs AI evaluation and observability - Model evaluation - Prompt testing - Hallucination detection - Latency monitoring - Cost tracking - Output quality assessment - AI observability - Langfuse ## From one workflow to production. We start with a single workflow and a working agent you can review. ### Discovery Map the workflow, the systems it touches, and where people step in today, the systems and data involved, and what good looks like. Discovery notesDone - Workflow map - Systems and access list - What the agent may and may not do ### Scope & architecture Agree the architecture, success measures and delivery milestones. Solution designDone - Agent architecture - Approval rules - Success measures ### Build Build against real data, with a working version reviewed at each milestone. Working buildDone - Working agent on real data - Evals against real cases - Review at each milestone ### Ship & support Roll out to production with monitoring and a defined support window. Production rolloutDone - Production rollout - Audit trail and monitoring - Defined support window ## Case spotlight. An agent platform in production. AI · Agent platforms ### An agent platform that cut manual effort by about 40%, with a person approving every high-stakes action. Autonomous execution across business systems only works if there is a real checkpoint before consequential actions, not full automation without oversight. An agent platform that retrieves relevant data, reasons over it, and proposes an action, routing anything high-stakes through a human approval step before execution. [Read the case study](/case-studies/enterprise-ai-agent-platform.html) ~40%Less manual effort Built in Human approval checkpoints 1 Agent platform across systems ## Questions buyers ask about agents. ### How is an AI agent different from RPA or a chatbot? RPA follows fixed scripts, and a chatbot answers questions. An agent reads the request, gathers what it needs across your systems, and proposes or takes the next step, within limits you set. ### What stops an agent from taking a wrong action? Every action is scoped and permissioned. Low-risk actions can run automatically; high-stakes ones go to a person for approval before they execute, and every action is logged. ### What about made-up answers? Agents work from your systems and documents, and check their output against source systems before it is treated as final. Where answers come from documents, each one traces back to its source. ### Which AI models do you use? We evaluate models against your real workflow and choose per task. The architecture is designed so models can change later without rebuilding the product. ### How do you handle our data? Access is scoped to what the system needs, with role-based permissions and audit trails where records change. We agree data handling during discovery. ### How long does a project take? It depends on scope and integrations. We agree milestones up front and review a working version at each one. ## Ready to turn your vision into reality? Tell us the workflow, the systems it touches, and where people step in today. We'll show you what an agent would do. [Book a call](https://calendly.com/abbylester/30-mins-meeting) [See the work](/work/index.html) Or email [contact@avinyalabs.co](mailto:contact@avinyalabs.co)