AI Agents, Workflows & Agentic OS

Build AI agents that fit your systems, workflows, and teams.

From workflow design to production deployment, we build AI agents that connect with your existing systems, automate multi-step business processes, and scale through a governed Agentic OS built for visibility, control, and measurable outcomes.

Our clients

Are AI agents the next step for your business?

66%

of businesses using AI Agents report increased productivity, 57% report cost-savings, 55% faster decision-making, and 54% improved customer experience.

87%

of businesses agree that AI agents are driving a new era of process transformation.

3x

higher growth in revenue is seen in industries most exposed to AI compared with those least exposed.

Build, connect, and govern AI agents across real business workflows

AI Agents, Workflows & Agentic OS

AI agent discovery and strategy

Before development begins, we help you identify where AI agents can create measurable value. This includes understanding workflow fit, business impact, data readiness, system dependencies, and the level of oversight needed before an agent can operate safely.

AI Agents, Workflows & Agentic OS

Custom AI agent development

We design and build AI agents around your business data, tools, rules, and goals. These agents can retrieve information, analyze context, generate outputs, trigger actions, and support teams inside clearly defined workflows.

AI Agents, Workflows & Agentic OS

Agentic workflow automation

We connect agents into multi-step workflows that reduce manual work and improve turnaround time. This helps move work across teams, systems, approvals, and exceptions without adding more disconnected tools.

AI Agents, Workflows & Agentic OS

Multi-agent system architecture

For more complex processes, we design systems where multiple agents work together with defined roles. One agent may retrieve information, another may validate it, another may draft the output, and another may route it for review or action.

AI Agents, Workflows & Agentic OS

Agentic OS implementation

We help set up the operating layer needed to manage AI agents as they scale. This gives teams visibility into who owns each agent, what it can access, where approvals are required, and how performance is tracked.

AI Agents, Workflows & Agentic OS

Secure deployment and optimization

We support rollout with the controls needed for real business use, then improve agents based on workflow performance, user feedback, accuracy, cost, and adoption signals.

Struggling to gain real business results from AI pilots?

The gap between a working pilot and an agent that moves the business is where most programs stall. The discovery workshop is where we close it.

A proven path to AI agents that deliver business results

01

01 Plan & design

AI strategy workshop

Designed for decision-makers who want to understand where AI agents can create real business value. In one collaborative session, we identify high-impact workflows, assess readiness, and outline the first practical steps for adoption.

Timeline: 1 day

Deliverables: Readiness report | Use-case map | Prioritized roadmap | KPI recommendations

Discovery and design phase

Create a clear blueprint for your first agentic AI implementation. This includes workflow mapping, data and system readiness review, autonomy boundaries, integration needs, and solution design.

Timeline: 1–2 weeks

Deliverables: Workflow map | ROI model | Solution blueprint | Data readiness view | Implementation plan

02 Transform

Rolling out the first AI agent

Select, build, and validate your first AI agent inside a defined business workflow. We track time, cost, efficiency, accuracy, and adoption signals to understand whether the agent is ready for wider rollout.

Timeline: 4–6 weeks

Deliverables: Working agent pilot | KPI baseline | Performance report | Risk checklist | Next-step recommendation

End-to-end workflow automation

Develop an agentic workflow that connects AI agents with the systems, tools, approvals, and data sources needed to complete a multi-step business process.

Timeline: 6–10 weeks, depending on workflow and integration complexity

Deliverables: Controlled workflow pilot | Integration plan | Approval paths | Monitoring setup | Team handoff guide

03 Scale

Scale AI agents and workflows

Expand from one validated workflow to multiple agents, teams, systems, and use cases. This stage focuses on Agentic OS implementation, governance, monitoring, cost control, evaluation, and continuous optimization.

Timeline: 10 weeks onwards, depending on rollout scope

A proven path to AI agents that deliver business results

step 1

Plan & design

AI strategy workshop

Timeline: 1 day

Deliverables: Readiness report | Use-case map | Prioritized roadmap | KPI recommendations

Discovery and design phase

Create a clear blueprint for your first agentic AI implementation. This includes workflow mapping, data and system readiness review, autonomy boundaries, integration needs, and solution design.

Timeline: 1–2 weeks

Deliverables: Workflow map | ROI model | Solution blueprint | Data readiness view | Implementation plan

step 2

Transform

Rolling out the first AI agent

Timeline: 4–6 weeks

Deliverables: Working agent pilot | KPI baseline | Performance report | Risk checklist | Next-step recommendation

End-to-end workflow automation

Develop an agentic workflow that connects AI agents with the systems, tools, approvals, and data sources needed to complete a multi-step business process.

Timeline: 6–10 weeks, depending on workflow and integration complexity

Deliverables: Controlled workflow pilot | Integration plan | Approval paths | Monitoring setup | Team handoff guide

step 3

Scale

Scale AI agents and workflows

Timeline: 10 weeks onwards, depending on rollout scope

What AI agents, workflows, and Agentic OS can do for your business

Reduce manual work across teams

Automate repetitive tasks, document reviews, approvals, and system updates. In high-volume workflows, this can reduce manual effort by up to 70%.

Speed up business workflows

Use agents to retrieve data, validate inputs, draft outputs, and route exceptions, helping multi-step workflows move 40–60% faster where repetitive steps can be automated.

Improve visibility and control

Track what each agent does, what systems it uses, where approvals are needed, and how each workflow is performing.

Scale operations without adding overhead

Increase output across support, sales, finance, operations, HR, and IT without adding more manual processes or disconnected tools.

Make decisions faster and more consistent

Use agents to analyze data, summarize context, detect patterns, and recommend next actions based on your business information.

Find the right workflows for AI agents.

Book Discovery Call

Types of AI agents we can build

Workflow and process automation agents

Automate repetitive, rule-based, and document-heavy tasks across teams and systems. These agents help reduce manual work, speed up approvals, and keep processes moving without constant human follow-up.

Use cases: Invoice validation, purchase order processing, CRM updates, employee onboarding, expense review, approval routing, scheduling, and internal reporting.

Conversational and experience agents

Deliver faster support and better experiences through chat, voice, email, or internal communication tools. These agents can answer questions, retrieve context, draft responses, qualify requests, and escalate complex issues to the right team.

Use cases: Customer support automation, internal IT helpdesk, lead qualification, order status updates, meeting reminders, employee support, and onboarding assistance.

Knowledge and document agents

Help teams find, summarize, extract, and act on information across internal documents, policies, reports, contracts, knowledge bases, and business records. These agents reduce search time and improve access to trusted business knowledge.

Use cases: Policy Q&A, contract summarization, compliance document review, report drafting, knowledge base search, clause extraction, and internal process support.

Predictive and decision intelligence agents

Analyze historical and real-time data to identify patterns, forecast outcomes, and recommend next actions. These agents support faster, more informed decisions across sales, finance, operations, risk, and customer teams.

Use cases: Sales forecasting, churn prediction, demand planning, credit risk scoring, anomaly detection, resource planning, and performance reporting.

Vision and data agents

Extract, validate, classify, and analyze information from forms, invoices, reports, scanned documents, images, and visual inputs. These agents help improve accuracy, reduce manual review, and turn unstructured data into usable business information.

Use cases: Invoice data extraction, document classification, report digitization, product defect detection, form processing, ID verification, and visual quality checks.

Orchestrator and multi-agent systems

Coordinate multiple specialized agents across larger business workflows. One agent may retrieve information, another may validate it, another may draft an output, while another triggers the next action or routes the task for human approval.

Use cases: Customer lifecycle management, supply chain coordination, claims intake, multi-department workflow automation, sales-to-support handoffs, cross-system data synchronization, and agentic workflow orchestration.

The Agentic OS your AI agents need before they scale

Agent registry

Track every AI agent by purpose, owner, workflow, tools, permissions, risk level, and business KPI, so your teams know exactly where agents are deployed and what they are responsible for.

Workflow orchestration

Define how agents move work across systems, teams, approvals, and exceptions. This helps turn isolated agents into connected workflows that complete multi-step business processes.

Tool and system access

Control how agents connect with CRMs, ERPs, ticketing tools, APIs, databases, documents, and internal platforms, so automation happens securely inside your existing environment.

Human approval paths

Add review checkpoints for sensitive actions, customer communication, financial decisions, compliance-heavy work, and high-risk outputs.

Monitoring and audit logs

Capture what each agent did, what data it accessed, which tools it used, what decision it made, and what outcome it produced.

Evaluation and optimization

Measure accuracy, reliability, task completion, cost, latency, adoption, and business impact, then improve agents based on real workflow performance.

AI agent use cases across industries

Financial services
Healthcare
Retail and ecommerce
Manufacturing
Logistics
Legal services
Software and information technology

Drive accuracy, compliance, and faster decision-making across financial operations with AI agents that support reporting, risk review, and customer workflows.

Automate reporting, reconciliation, and compliance tracking.
Detect transaction anomalies and potential fraud patterns.
Forecast financial trends, credit risk, and portfolio performance.
Streamline customer onboarding and support through conversational agents.
Coordinate multi-agent workflows for audit preparation, portfolio review, and risk analysis.

Reduce administrative workload, improve operational visibility, and support faster access to patient and clinical information.

Automate appointment scheduling, documentation, and claims processing.
Assist care teams with patient record summaries and knowledge retrieval.
Monitor patient records, device data, and operational signals for anomalies.
Manage medical inventory, supply forecasting, and administrative follow-ups.
Coordinate intake, claims, and care team workflows through agentic automation.

Improve customer experience, inventory visibility, and operational efficiency across digital and physical retail workflows.

Analyze purchase patterns for demand forecasting and inventory planning.
Automate inventory updates, returns, refunds, and customer support workflows.
Personalize recommendations, offers, and customer communication.
Support dynamic pricing, promotion planning, and product performance analysis.
Coordinate fulfillment, logistics, and support workflows through orchestrator agents.

Enable predictive operations, improve output, and reduce downtime through intelligent automation across production and supply chain workflows.

Predict maintenance needs and detect equipment anomalies early.
Automate production scheduling, resource allocation, and reporting.
Use vision agents for quality control, defect detection, and inspection tasks.
Analyze equipment and process data to improve operational performance.
Coordinate supply chain, procurement, and production workflows through multi-agent systems.

Improve visibility, scheduling, and efficiency across transportation, warehousing, and supply chain operations.

Optimize delivery routes, fleet utilization, and shipment scheduling.
Automate shipment tracking, billing, documentation, and status updates.
Manage warehouse workflows, order fulfillment, and inventory movement.
Analyze logistics performance data for delays, risks, and proactive maintenance.
Coordinate real-time communication between dispatch, warehouse, carrier, and customer support agents.

Streamline document-heavy workflows and improve speed, accuracy, and consistency across legal operations.

Automate contract review, clause extraction, and document classification.
Summarize case files, agreements, policies, and legal research.
Support compliance checks and policy alignment reviews.
Manage high-volume document review through knowledge and workflow agents.
Coordinate multi-agent review systems for contracts, discovery, and legal operations.

Accelerate delivery, improve reliability, and reduce repetitive work across software, IT, and service management workflows.

Automate testing, deployment, monitoring, and release documentation.
Triage incidents, detect anomalies, and summarize root-cause signals.
Streamline DevOps collaboration through workflow orchestration.
Generate technical documentation, sprint summaries, and post-release reports.
Support internal knowledge retrieval, access requests, and IT helpdesk workflows.

Download our AI Workshop Report Template

A detailed snapshot of AI readiness, data maturity, and governance status
A list of high-impact AI use cases mapped to business objectives
A roadmap outlining timelines, effort, and next steps for adoption

Success Stories in Spotlight

50% faster discovery with an AI-powered knowledge sharing agent

AgricultureArtificial Intelligence

View case study

"I’m going over testing feedback about the chatbot; overall, testers found the product to be fantastic. We’re very happy with this."

Chris Head

Director of IT, CCOF

60% reduction in response times through AI-automated email workflows

Hospitality & TravelArtificial Intelligence

View case study

WATCH TESTIMONIAL

Don Sgouridis

CIO, BBJ Latavola

50% reduction in support workload with AI-powered document retrieval

AgricultureArtificial Intelligence

View case study

"tkxel built an AI assistant that delivers accurate guidance fast and reduces manual work."

Director of Operations

Embedding agentic AI in Slack to eliminate workflow bottlenecks

Media & EntertainmentArtificial Intelligence

View case study

"tkxel built a Slack AI assistant that surfaces SOPs instantly and keeps our team productive."

Head of Operations

Built for real workflows, not isolated AI demos

Strategy before development

We do not start by building an agent. We first identify the right workflow, business case, KPI, data readiness, integration needs, and governance requirements, so every build has a clear path to value.

Full-stack AI delivery team

Our AI architects, engineers, integration specialists, cloud experts, QA teams, and product designers work together to move agentic workflows from concept to production with fewer handoffs and less delivery risk.

Built-in guardrails for agentic workflows

We build AI agents with defined policies, access controls, human approval paths, monitoring, and evaluation checks, so agents can take action without losing reliability, security, or control.

Measurable outcomes from day one

Every agentic workflow is tied to a business metric such as reduced manual work, faster response time, lower cost, improved accuracy, better visibility, or increased team capacity.

Tools & technologies

LLMs
Agent Frameworks
Vector Stores

OpenAI

Anthropic

Gemini

DeepSeek

Llama

Mistral

Langchain Langgraph

Open AI Agent Builder

Microsoft Copilot Studio

Qdrant

Weaviate

Pg Vector

We’ve been recognized by the best, year after year

AMERICA’S FASTEST GROWING COMPANY

Top 15 inspiring workplaces for 2026

titan business PLATINUM award AI & AUTOMATION

FINANCIAL TIMES

mogul people leader

FORBES COACHES COUNCIL

ISO 27001 CERTIFIED

ISO 20000 CERTIFIED

ISO 9001 CERTIFIED

CMMI DEV 3 CERTIFIED

AI Agent Development Services for Production Ready Solutions

Custom AI agents designed, built, integrated, and deployed for real business use.

We build agents around your data, systems, tools, and business rules from internal copilots to autonomous agents that handle defined tasks. Every agent is tested before it reaches production and measured after it goes live.

We work with SaaS, healthcare, fintech, and ecommerce companies across the US, Europe, and global markets.

Every engagement starts with one question: what should the agent do, what does it need to do it, and how will we know it works?

Who We Work With

CTOs and CIOs: agents that fit your existing stack, no forced platform changes.

Product leaders: AI built for real products, not isolated prototypes.

Operations leaders: less repetitive work, with people involved where judgment is required.

Founders and innovation teams: a practical path from concept to production.

Finance and budget owners: clear scope, costs, and success measures upfront.

What We Build

AI Agent Type	What It Does
AI Copilots	Support research, drafting, analysis, and tasks while keeping people in control.
Autonomous AI Agents	Handle defined tasks independently within set permissions.
Knowledge Agents	Retrieve and reason over approved business data and policies.
Decision Support Agents	Provide recommendations based on defined business rules.
AI Product Agents	Embed agent capabilities into SaaS products and internal tools.
AI Agent Integrations	Connect agents to APIs, databases, CRMs, ERPs, and EHRs.

We choose models, tools, and data sources based on what the agent needs to accomplish.

Our AI Agent Development Process

01. Define the Agent: The business requirement becomes a technical spec that defines the purpose, inputs and outputs, data sources, system access, approval points, and success criteria.

02. Design the Architecture: We translate those requirements into a technical blueprint before development starts. What it covers is detailed below, under Architecture.

03. Develop and Integrate: Engineers build the agent logic and connect it to the systems it needs APIs, tools, authentication, error handling, integration-ready agent, not a demo.

04. Test and Evaluate: The agent runs against defined acceptance criteria before it goes near production. Full methodology is below, under Testing.

05. Deploy and Optimize: Once requirements are met, we handle deployment, logging, monitoring, and cost tracking, then use live data to keep improving it.

Architecture Built Around the Use Case

Model and prompt engineering: chosen for capability, latency, reliability, and cost.
Retrieval and business knowledge (RAG): responses grounded in approved sources.
Tools and function calling: each tool scoped to what’s needed and permitted.
Memory and context: history and task state, without retaining unnecessary data.
Human-in-the-loop controls: escalation rules built in from the start, not bolted on.

Integrating With Your Existing Stack

A production agent needs to work with the systems your business already uses. We connect it to CRM, ERP, and EHR systems, support and ticketing platforms, internal APIs, databases, document repositories, and custom applications, handling the authentication and error handling each integration needs.

Testing Before Production

Standard software testing isn’t enough for AI systems. We build evaluation criteria around the agent’s actual job and test it against representative inputs, edge cases, and failures, covering task completion, accuracy, groundedness, instruction adherence, tool selection and execution, escalation behavior, response time, and cost per task.

That’s evidence the agent is genuinely ready, not just convincing in a demo.

Built for a Live Environment

A system operating inside your actual environment needs security, authentication, data access controls, observability, and version control from the start. That is what separates a working agent from a demonstration.

Controlling Costs From the Start

Model choice, context size, tool usage, and request volume all affect running costs. We track token consumption, API and retrieval costs, infrastructure needs, and human review time during development, so cost decisions happen early.

Improving After Launch

We use production data to refine prompts, improve retrieval and tool reliability, speed up responses, lower costs, and handle edge cases better, driven by actual performance, not assumptions.

What We Avoid

Over-automating high-impact decisions: Real financial or customer risk gets review points.
Building before defining the requirement: Responsibilities and success criteria come first.
Adding technology without a purpose: No extra models or integrations without a need.
Treating evaluation as an afterthought: Agents are tested against real work.
Ignoring operating costs: Usage, tool calls, and review time are considered from day one.

Industries We Serve

Industry	Development Focus
SaaS	Embedded into products, internal tools, and customer experiences
Healthcare	Knowledge access, admin support, controlled data access, and human review
Fintech	Decision support, customer operations, and document processing
Ecommerce	Customer assistance, product discovery, and internal support tools

The implementation changes by industry, but the principles stay the same: define, architect, integrate securely, evaluate, and optimize.

Why Choose tkxel

Engineering led: Agents are built and tested as software systems, not demos.

Requirements first: We start with the job, then choose the technology.

Production focused: Security, integrations, evaluation, and costs are considered from day one.

Flexible architecture: No forced single model or single stack approach.

Measurable: Performance is defined upfront and improved using real data.

We help define the agent, design its architecture, build the integrations, evaluate performance, and get it live.

Ready to Transform Your Business with AI

Let’s explore how AI can help you move faster and smarter.

Ready to build your next AI agent?

“tkxel completely transformed the way we manage our customer relationships. Their customized CRM system streamlined our processes and improved customer satisfaction. We highly recommend their services to any business looking for real results.”

Nick Drogo

Global Director IT, Knowles

“They helped us build a docketing app with an intuitive user interface, allowing our attorneys to track over 10,000 U.S. and international patent systems.”

Robert K Burger

COO, Sterne Kessler

“tkxel has proven beyond par that they excel not just in building and integrating with our team but building at a level that is at par with any US development team. Working with tkxel is one of the best decisions we have made.”

Umair Bashir

CTO, Replenium

“tkxel shared our vision right from the get go, and helped us achieve the unthinkable through perseverance and a thorough attention to detail. Their team was highly professional and possessed a firm grasp on technicalities, a combination that is hard to find in the industry.”

Pam Chitwood

Product Manager, ABB

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Nick Drogo

Global Director IT, Knowles

“They helped us build a docketing app with an intuitive user interface, allowing our attorneys to track over 10,000 U.S. and international patent systems.”

Robert K Burger

COO, Sterne Kessler

Umair Bashir

CTO, Replenium

Pam Chitwood

Product Manager, ABB

Frequently asked questions

What is agentic AI development?

Agentic AI development is the process of designing, building, and deploying AI agents that can understand context, use tools, follow business rules, and complete tasks across defined workflows. Unlike basic chatbots, agentic systems can retrieve information, call APIs, trigger actions, route exceptions, and work with human approval where needed.

What types of custom AI agents can tkxel build?

tkxel builds custom AI agents for workflow automation, customer support, internal knowledge search, document processing, decision intelligence, vision-based use cases, and multi-step business processes. These agents can be designed around your systems, data, policies, and team workflows instead of using a generic one-size-fits-all setup.

How does AI workflow automation work with existing business systems?

AI workflow automation connects agents with tools such as CRMs, ERPs, ticketing systems, databases, APIs, documents, and internal platforms. Agents can retrieve data, validate inputs, draft outputs, update systems, escalate exceptions, and move work across teams while maintaining visibility, access controls, and audit logs.

When do businesses need multi-agent systems instead of one AI agent?

Multi-agent systems are useful when a workflow has several specialized steps. For example, one agent may retrieve information, another may validate it, another may generate a response, and another may route the task for approval or system action. This approach works well for complex processes such as claims intake, contract review, supply chain coordination, sales-to-support handoffs, and cross-system workflow orchestration.

Can autonomous AI agents operate without human oversight?

Some autonomous AI agents can complete low-risk tasks without constant human involvement, but high-risk actions should include human-in-the-loop controls. For sensitive decisions, customer communication, financial approvals, compliance-heavy work, or system changes, human review checkpoints help improve reliability, accountability, and trust.

What technologies are used to build enterprise AI agents?

AI agents may use frameworks and patterns such as LangChain, LangGraph, CrewAI, AutoGen, RAG, LLM orchestration, tool/function calling, agent memory, and Model Context Protocol (MCP), depending on the use case. The right architecture depends on workflow complexity, integration needs, governance requirements, latency, cost, and how much autonomy the agent needs.

Why choose an AI agent development company instead of building agents in-house?

An AI agent development company brings the strategy, architecture, engineering, integration, QA, deployment, and governance experience needed to move from AI demos to production-ready systems. With agentic AI consulting and AI orchestration services, tkxel helps define the right use case, design the agent workflow, connect it with existing systems, add guardrails, and measure business outcomes after deployment.