Custom AI Agent Development
Design and build purpose-built AI agents around your business processes, users, information and applications.
Grawlix develops custom AI agents and agentic AI systems that understand goals, reason through tasks, retrieve business knowledge, use approved tools, connect with APIs and help automate complex business workflows.
An AI agent is a software system designed to interpret a goal, reason about what needs to happen, use available information and interact with approved tools or applications to perform tasks. Unlike a basic chatbot that primarily responds to messages, an AI agent can be designed to execute multi-step workflows such as retrieving information, calling APIs, updating business systems, preparing outputs and coordinating tasks while operating within defined permissions and controls.
Grawlix designs and develops AI agents that combine language models, business knowledge, tools, APIs, workflows and application logic to support practical business use cases.
Design and build purpose-built AI agents around your business processes, users, information and applications.
Create coordinated systems where specialized agents collaborate on research, analysis, workflow and execution tasks.
Develop AI agents that work with approved enterprise knowledge, software, databases, APIs and internal workflows.
Connect agents with relevant organizational knowledge using retrieval architectures and approved information sources.
Support lead research, qualification, sales knowledge, follow-up preparation and suitable CRM workflows.
Build AI agents that retrieve relevant knowledge, assist with responses, classify requests and support service workflows.
Combine AI reasoning with APIs and business rules to support repetitive multi-step operational workflows.
Create agents that can retrieve, summarize, compare and work with appropriate business documents and information.
Connect agents with existing SaaS platforms, CRM, ERP, web apps, mobile apps, databases and APIs.
An AI agent can be designed to move beyond simply generating an answer by reasoning about a task, selecting approved tools, performing actions and evaluating the result.
Complex workflows do not always need to be handled by one general-purpose agent. A multi-agent architecture can assign specialized responsibilities to different agents and coordinate their work through an orchestrator.
Agentic AI can be applied to appropriate workflows where software needs to interpret information, determine steps and interact with approved business systems.
Analyze lead information, enrich context and support qualification workflows.
Gather relevant account information and prepare structured sales research.
Retrieve knowledge, categorize requests and support service workflows.
Help employees navigate approved HR, IT and organizational knowledge.
Retrieve, summarize, classify and route appropriate business documents.
Gather and organize relevant information from approved data sources.
Support suitable CRM research, data preparation and workflow tasks.
Assist with project information, task preparation and operational coordination.
Support appropriate document and information workflows with defined controls.
Assist with product discovery, customer questions and appropriate commerce workflows.
Provide conversational access to approved internal business knowledge.
Coordinate suitable multi-step tasks across APIs, applications and business systems.
Imagine a sales representative receives a new enterprise lead. Instead of manually researching the company, reviewing CRM history, finding relevant service information and preparing a follow-up, an AI agent could assist with the workflow.
The agent could gather approved information, prepare a structured brief and create suggested next steps while keeping the user in control of important decisions.
We start with the business process and then design the agent, tools, data access and application architecture around it.
Identify the workflow, users, goals and expected business outcome.
Map decisions, knowledge, tools, applications and human checkpoints.
Design model, memory, RAG, tools, permissions and orchestration.
Develop the agent logic, integrations, prompts and user experience.
Test workflows, tool use, outputs, failure cases and controls.
Launch, monitor and continuously improve agent performance.
AI agent architecture can combine language models, orchestration frameworks, retrieval, APIs, application development and cloud infrastructure.
Grawlix develops AI agent solutions around industry workflows, business systems, knowledge and operational requirements.
Knowledge assistance, administrative workflows, document intelligence and suitable healthcare operations.
Document, knowledge and operational AI agents with appropriate business controls.
Shopping assistance, customer support, product discovery and commerce workflows.
Embed AI agents and agentic workflows directly inside software products and SaaS platforms.
Support operational information, workflow coordination and logistics knowledge processes.
Learning assistance, knowledge navigation and educational workflow agents.
Property discovery, lead assistance, document workflows and customer engagement.
Technical knowledge, SOP assistance, operations and manufacturing workflow agents.
A useful enterprise AI agent may require reasoning, knowledge retrieval, tool integration, application development, business rules, permissions, monitoring and human checkpoints.
Grawlix combines AI engineering with software development, web and mobile applications, APIs, SaaS, cloud infrastructure and automation to build complete agentic AI systems.
From identifying the workflow to developing, integrating and deploying a working AI agent.
Clear answers about AI agents, agentic AI, multi-agent systems and enterprise AI agent development.
An AI agent is a software system designed to interpret a goal, reason about appropriate steps and interact with approved information, tools or applications to perform tasks.
Agentic AI refers to AI systems designed to pursue objectives through multi-step reasoning and actions rather than only generating a single response. Agentic systems can use tools, retrieve information and interact with software within defined controls.
An AI agent development company designs and develops AI-powered software agents around business workflows. This can involve language models, RAG, tool integration, APIs, orchestration, application development, testing and deployment.
Grawlix develops custom AI agents, enterprise AI agents, multi-agent systems, knowledge and RAG agents, customer support agents, sales agents, document agents, workflow agents and AI agent integrations.
A chatbot is typically designed around conversational interaction. An AI agent can additionally be designed to determine steps, use approved tools, retrieve information and perform actions as part of a multi-step workflow.
A multi-agent system uses multiple specialized agents that coordinate to complete a broader task. For example, one agent might research information, another analyze it and another perform an approved action.
Yes. Depending on the available integrations and permissions, AI agents can connect with suitable CRM, ERP, SaaS platforms, databases, websites, internal applications and APIs.
Yes. Agents can be designed to retrieve relevant information from approved company knowledge sources using architectures such as Retrieval-Augmented Generation.
AI agents can support suitable workflows that involve interpreting information, determining steps and interacting with approved tools. The appropriate level of automation depends on the process, risk and required human oversight.
Yes. Grawlix can build AI agents that help retrieve support knowledge, classify customer requests, prepare responses and support suitable service workflows.
Yes. Sales AI agents can support workflows such as lead research, qualification, account research, CRM information retrieval and preparation of sales briefs or follow-up drafts.
Yes. Human-in-the-loop checkpoints can be incorporated into agent workflows so that selected actions require review or approval before execution.
AI agents can be built using suitable models from different providers. Model selection depends on reasoning requirements, quality, latency, privacy, cost, tool use and deployment needs.
Development time depends on workflow complexity, number of tools, data sources, integrations, user experience, testing requirements and the level of autonomy required.
Start by identifying a specific repetitive or knowledge-intensive business workflow, the systems involved, decisions required, available data and the actions the agent should be permitted to perform. The architecture can then be designed around those requirements.
Talk to Grawlix about custom AI agents, agentic AI, multi-agent systems, enterprise AI assistants and intelligent workflow automation.
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