Agentic AI Consulting Services for Real Enterprise Outcomes
Our agentic AI consulting and implementation services help enterprises build autonomous agents that execute complex workflows, make context-aware decisions, and operate with minimal human oversight, turning operational bottlenecks into self-managing systems.
Process Efficiency Gain
Faster Business Outcomes
Lower Operational Expenses
AI Agents Built to Automate and Accelerate Workflows
Our AI agents handle the work that slows teams down (retrieving information, analyzing datasets, summarizing contracts, masking sensitive data) at enterprise volume. Our AI agent solutions get scoped access to ERP, CRM, and documents, and are scored on historical cases before go-live.
- Flags clause deviations and proposes compliant fixes
- Keeps the reviewer in command of decisions
- Learns from accepted corrections to automate routine work
- Queries structured enterprise data using natural language
- Visualizes data trends dynamically for faster decisions
- Plugs into existing workflows with minimal configuration
- Quickly processes and summarizes legal files
- Ensures secure, private processing without data storage
- Delivers summaries directly via email for user convenience
- Redacts sensitive details from your documents automatically
- Ensures compliance with privacy regulations like GDPR & HIPAA
- Customizes redaction fields to meet specific needs
- Detects arithmetic errors, chart misalignments in documents
- Provides categorized reports with AI-generated explanations
- Ensures secure document processing with no data storage
Jennifer: Calling Agent
- Handles inbound and outbound calls, acting on voice instructions
- Offers assistance for scheduling and information gathering
- Scale phone operations without proportional staffing increases
Assess Your AI Maturity
Evaluate your enterprise readiness across AI/ML foundations, Generative AI capabilities, and AI Agent deployment. Get personalized recommendations from Kanerika's AI experts.
Agentic AI Services: Consulting, Development and Managed Agents
We cover the full agentic AI lifecycle: strategy, architecture, orchestration, and production deployment. For teams still building their roadmap, that starts with agentic AI strategic advisory services before any build work begins.
AI Agent Development Services
We design, build, and deploy goal-driven AI agents that reason through tasks, use tools, and adapt to changing conditions without requiring constant human input.
- Designs agents with task planning and memory
- Builds tool-use and retrieval-augmented agent architectures
- Deploys agents integrated with enterprise data systems
Multi-Agent Orchestration
Every orchestration architecture is a custom agentic AI solution, scoped to your specific workflows, not a templated multi-agent pattern.
- Orchestrates cross-functional agents for parallel task execution
- Enables autonomous decisions with built-in business logic
- Supports real-time agent collaboration and task routing
Agentic AI Implementation
Integration is a first-class workstream in every engagement. Kanerika’s agentic AI integration consulting services cover ERP, CRM, cloud data platforms, and internal APIs.
- Assesses enterprise readiness across data, systems, and skills
- Designs secure agent architectures with governance
- Integrates agents into ERP, CRM, and cloud environments
Custom AI Agent Development Services
As an AI agent development company, we build agents one workflow at a time, with measured baselines and clear limits. See what AI agent development costs.
Pick the Workflow
We rank workflows by volume, clear rules, and cost of errors, then start where a baseline is measurable.
Design the Agent
We define the tools it can call, what it remembers, and where a person approves before it acts.
Connect and Test
Agents get scoped access to ERP, CRM, and documents, then are scored on historical cases before go-live.
Run and Improve
Monitoring, audit logs, and a review loop keep accuracy up as your data and processes change over time.
Custom Agentic AI Solutions Built Around Your Workflows
Most AI agent projects fail because they’re built for a demo, not for how your business actually runs.
Your Data, Your Agents
Every agent is trained on your systems and data. Nothing generic, no off-the-shelf logic applied to your workflows.
Integrated, Not Bolted On
Agents connect directly to your ERP, CRM, and cloud platforms. No parallel systems to manage or maintain.
Governed from Day One
Access controls, audit trails, and explainability are built into the architecture before the first agent goes live. See our guide to agentic AI governance.
Tied to Real Metrics
Every deployment is scoped to specific outcomes like processing time, cost per task, and tracked against your KPIs.
Agentic AI Deployments Delivering Measurable Enterprise ROI
Enterprises across manufacturing, logistics, and financial services have used our agentic AI solutions to reduce manual workloads and accelerate decision cycles. These deployments run in live production environments, not pilot programs.
AI/ML & Gen AI
43% Faster Document Retrieval for an Investment Bank
Impact:
- 43% Faster information retrieval
- 100% Role-based compliance achieved
- 35% Higher workforce efficiency
AI/ML & Gen AI
30% Faster Inventory Reconciliation with AI
Impact:
- 30% Faster Inventory Reconciliation
- 50% Reduction in Time-to-Insight
- 10+ Recurring Variance Patterns Automatically Detected
AI/ML & Gen AI
65% Self-Service Resolution with an AI Support Agent
Impact:
- 42% Reduction in ticket volume
- 31% Decrease in cost per ticket
- 25% Increase in member satisfaction
IMPACT Framework for Our Agentic AI Implementation Services
At Kanerika, we use the IMPACT methodology to ensure our Agentic AI solutions deliver measurable outcomes and enterprise-scale impact.
Tools and Technologies
We leverage cutting-edge agentic AI frameworks to build intelligent autonomous agents, streamline workflows, and drive business efficiency.
Agentic AI for Enterprise Use Cases Across Key Industries
Why Enterprises Choose Our Agentic AI Consulting Services
We ship agents that run in real client environments, not proofs of concept. Our full-service agentic AI services company model means every engagement runs from first scoping through live production. Our agentic AI transformation consultants rank workflows by volume, clear rules, and cost of errors, then start where a baseline is measurable.
We build agents that operate in live enterprise environments, not sandbox demos. KlarityIQ, Karl, Alan, Susan, Mike, and Jennifer run in active client deployments today.
Our agentic AI consultancy handles strategy, architecture, development, integration, and ongoing operations under one engagement model, with no handoffs to third-party implementers.
As a certified Microsoft Solutions Partner for Data and AI, we build agentic AI solutions on Azure AI, Copilot Studio, and Power Platform with enterprise governance built in.
Empowering Alliances
Our Strategic Partnerships
The pivotal partnerships with technology leaders that amplify our capabilities, ensuring you benefit from the most advanced and reliable solutions.
Frequently Asked Questions (FAQs)
What are agentic AI consulting services, and what does an engagement typically include?
Agentic AI consulting helps enterprises identify which business processes are suitable for autonomous AI agents, then designs and deploys those agents in production. A typical engagement covers readiness assessment, agent architecture design, tool selection, multi-agent orchestration planning, enterprise integration, and governance setup. Kanerika’s agentic AI consulting practice runs end-to-end, from the first scoping call through to live production monitoring and optimization. You may also see this work called AI agent consulting.
What types of agentic AI services does Kanerika offer?
Kanerika’s agentic AI services cover four core areas: autonomous agent development, multi-agent orchestration, enterprise integration, and ongoing agent operations. Each service addresses a distinct stage of deployment. Standalone agents handle specific tasks like document retrieval or PII redaction. Orchestrated agent networks handle cross-functional workflows that span multiple systems. All services are available as part of a full implementation engagement or as targeted workstreams added to an existing AI program.
How does Kanerika approach AI agent development services for large enterprises?
Kanerika’s AI agent development services start with a process audit to identify tasks that benefit most from autonomous execution. From there, the team designs agent architectures using reasoning models, retrieval-augmented generation, and tool-use frameworks appropriate for the use case. Agents are tested in controlled environments before production rollout. Enterprises get fully documented agent logic, integration specifications, and governance controls that satisfy security and compliance review requirements. The agents run as enterprise AI agents, with AI agent integration into your existing applications and data sources.
What does agentic AI implementation consulting for enterprise actually involve?
Agentic AI implementation consulting for enterprise covers the technical and organizational work required to move from agent concept to production system. This includes system readiness assessment, agent design, API and data integration, testing against real workloads, staff enablement, and post-launch monitoring. Kanerika structures implementation through its IMPACT methodology, which sequences each phase to reduce deployment risk and accelerate time to measurable value.
Which agentic AI use cases are best suited for enterprises today?
High-volume, rule-bound, and data-intensive processes offer the clearest return. Document processing, compliance monitoring, customer inquiry routing, claims intake, inventory management, and financial reconciliation are all active agentic AI deployment areas for enterprise. Kanerika recommends starting with processes that have defined inputs, clear decision criteria, and measurable output quality, as these yield faster deployment and more defensible ROI within the first 90 days.
How is an agentic AI consultancy different from a general AI consulting firm?
A general AI consulting firm typically covers strategy, model selection, and roadmap planning. An agentic AI consultancy goes further: it designs, builds, and deploys agents capable of autonomous task execution. That requires engineering depth in agent frameworks, memory architectures, tool orchestration, and enterprise integration. Kanerika functions as an agentic AI consultancy with a dedicated delivery practice, not just advisory output. Clients receive working agents, not just strategy documents.
What should I look for when hiring an agentic AI consultant?
Look for demonstrated production deployments, not just whitepapers or pilot work. An agentic AI consultant should be able to show working agent architectures, describe how they handle failure states, explain their governance approach, and name specific enterprise systems they have integrated with. Ask about observability and monitoring practices. Kanerika’s consultants have shipped named agents running in live client environments across manufacturing, logistics, financial services, and healthcare.
What makes Kanerika a credible agentic AI development company for enterprise?
Three things stand out. First, Kanerika builds and operates its own production AI agents across industries, so the engineering team brings direct deployment experience to every client engagement. Second, Microsoft Solutions Partner certification for Data and AI provides verified technical depth on enterprise-grade platforms. Third, Kanerika holds ISO 27001 and ISO 27701 certifications, SOC 2 Type II compliance and a CMMI Level 3 appraisal. These cover the compliance requirements most regulated-industry enterprises set before approving a production AI deployment.
How much does agentic AI consulting cost?
Cost depends on scope. The main drivers are how many workflows the agents cover, how many systems they connect to, how ready your data is, your security and compliance requirements, and whether Kanerika also runs the agents after launch. A single-workflow first agent costs less than a multi-agent rollout across several teams. Kanerika sizes an engagement after a scoping call, once the workflow and integrations are clear.
How do I evaluate agentic AI consulting companies before committing?
Start with production evidence. Agentic AI consulting companies worth considering should be able to show named agents running in live enterprise environments, not just pilot case studies. Ask how they handle agent failure states, what their governance approach looks like, and which enterprise systems they have actually integrated with. Kanerika builds and operates its own production agents, Karl, Alan, Susan, Mike, and Jennifer, so the engineering team brings direct deployment experience to every client engagement. Shortlist agentic AI companies that can show production deployments, not only prototypes.
How long does it take to get a first AI agent into production?
Timelines depend on the workflow, the number of systems the agent touches, and how clean the underlying data is. A first agent on a single workflow includes testing on historical cases before go-live. Later agents usually go faster, because the integrations, monitoring, and approval flows are already in place, and each one is scoped against a measured baseline, so progress is easy to track.
Can the agents run inside our Microsoft 365 or Azure tenant?
Yes. As a Microsoft Solutions Partner for Data and AI, we build agents on Azure AI, Copilot Studio, and Power Platform, working with the data systems you already run rather than requiring a migration or a parallel platform. Users can reach them in tools like Microsoft Teams, and access controls and audit trails are built in from day one, so governance stays with your existing processes.
How do approvals and human escalation work?
Each agent has written limits on what it may do alone. Anything outside those limits, or below a confidence threshold, goes to a named person with the agent’s reasoning and sources attached, and every action is logged for audit. You decide where approvals sit, so high-risk steps always need a person before the agent acts. Limits can be adjusted as the agent proves itself.
How do you secure AI agents and control what they can access?
Each agent gets access only to the systems its job needs, and every action it takes is logged so you can audit it later. Higher-risk steps need a person’s approval before the agent acts. We build these controls into the design before the first agent goes live. Kanerika holds ISO 27001 certification and SOC 2 Type II compliance. For a closer look at the risks, read our guide to agentic AI vulnerability assessment and our overview of agentic AI governance.
How do you test and monitor AI agents?
Before go-live, we score each agent on historical cases from your own work and track its accuracy and how often it hands a case to a person. After launch, monitoring and audit logs show how it performs, and a review loop keeps accuracy up as your data and processes change. Our guide to AI agent evaluation covers the metrics in more detail.
What is multi-agent orchestration, and when does a workflow need it?
Some workflows need several agents working together. One agent reads documents, another checks the figures and a third drafts the response, while an orchestrator passes work between them and stops for a person when something is unclear. A single agent is enough when one set of tools covers the whole job. Agents also rely on shared protocols: MCP covers agent-to-tool communication and A2A covers agent-to-agent communication. Read more in our guides to AI agent orchestration and MCP vs A2A.
How is agentic AI different from RPA?
RPA bots follow fixed scripts. They suit stable, repetitive steps and stop working when a screen, file format or rule changes. An AI agent reads the situation, chooses which tool to use, handles exceptions and sends unclear cases to a person. The two can also work together: the agent decides what to do and an RPA bot carries out a routine step in a legacy system. Our guide to agentic automation compares the approaches in detail.
How do you prove an AI agent works before a wider rollout?
A proof of concept for an AI agent should answer one question: does it beat today’s baseline? We pick one workflow with a measurable baseline, build the agent and test it on historical cases. The results show where it performs well and where it needs a person, before you commit to a wider rollout. For common reasons AI projects stall after the pilot, read our guide to moving AI from pilot to production.
Can AI agents handle customer service?
Yes. In one of our deployments, an AI member-support agent reached 65% self-service resolution, cut ticket volume by 42% and lowered cost per ticket by 31%. Read the full case study. Complex cases still go to a person, with the agent’s reasoning attached. For a wider look at the tools available, see our guide to AI agents for customer support.
Can I hire dedicated agentic AI developers?
Yes. Our hire AI developers service provides experienced engineers for agentic AI projects, including single task agents and multi-agent workflow systems with human approval checkpoints. You can also have us design and build the agents end to end.
