Agentic AI Frameworks: Building Custom Agents

Agentic AI is where enterprise AI grows up. Instead of spitting out text or code, agents use context, plan steps, and take action toward a goal. The shift is simple to explain and big to execute. Less “what can AI generate” and more “what can AI finish for me.”

Enterprises need structure to do that at scale. That is where agentic AI frameworks come in. A strong agentic framework gives you orchestration, security, and deployment patterns that move you from a clever demo to production. This guide defines what is agentic AI, shows how AI agent frameworks differ from quick builders, maps real use cases with measurable outcomes, compares top agentic AI frameworks, and lays out a clear plan to build custom AI agents across your environment.

This is your blueprint for Agentic AI Frameworks: Building Custom Agents.

What is agentic AI?

Agentic AI is an approach where artificial intelligence stops being a passive tool and starts behaving like an active participant in your business. Instead of spitting out a single answer when asked, an agentic system is designed to pursue a goal from start to finish. It perceives inputs, reasons about what to do next, executes the right steps, and adapts when conditions change.

A well-designed agent can call APIs, monitor live data streams, update records in your systems, and keep workflows moving without a human babysitter. This is not about replacing people. It is about giving teams intelligent digital coworkers that can handle repeatable, multi-step tasks.

Compared to a single model that produces outputs in isolation, an AI agent framework connects those models to tools, data, and policies so work gets done across environments. Think of:

• A support agent that authenticates a customer, checks order history, issues a refund, and closes the ticket without escalation.

• A DevOps agent that spots a failing container, spins up a replacement, rebalances traffic on AI on Kubernetes, and logs the event for compliance.

• A finance agent that reviews transactions in real time, compares them to risk thresholds, and blocks suspicious activity before funds move.

Characteristics of agentic AI

Here are the traits that separate agentic AI from basic automation or chatbots:

• Contextual awareness. Agents ingest live data and adapt decisions as conditions change.

• Autonomous reasoning. Agents plan multi-step sequences, not just one-off responses.

• Outcome orientation. Agents are built to achieve specific goals with measurable success criteria.

• Continuous interaction. Agents can converse, call APIs, and collaborate with both humans and systems.

• Safety by design. Governance, policies, and audit trails are not bolted on later — they are embedded from the start.

Agentic AI is the shift from answering questions to delivering results. It is how AI stops being a tool on the side and starts becoming part of the operating system of your enterprise.

Agent builders vs agentic AI frameworks

When organizations begin exploring agentic development, they typically run into two types of tools: agent builders and agentic AI frameworks. Both are useful. Both solve different problems. Understanding where each fits can save months of wasted time and stalled pilots.

Agent builders

Agent builders are lightweight toolkits designed to help teams spin up small, single-purpose agents quickly. They are great for proof of concept work — like a chatbot that answers FAQs, a scheduling assistant that pulls data from a calendar, or a workflow bot that automates one repetitive task.

The appeal is speed. You get something functional in hours, not months. But what you gain in speed, you sacrifice in depth, governance, and enterprise readiness.

Strengths

• Easy to use for developers and even technical business users

• Fast to prototype and validate small ideas

• Low entry cost with open source options or lightweight licenses

Limitations

• Limited integration depth — usually just surface-level API connectors

• Cannot handle large-scale or mission-critical workloads

• Minimal security, compliance, or governance features

• Poor observability — little to no monitoring or analytics

Example

A support team builds a chatbot to handle password resets and FAQs during a pilot. It works for a small group of users. But once the pilot expands to thousands of concurrent sessions and must integrate with CRM, payment systems, and compliance tools, the builder collapses under the pressure.

Pitfall to avoid: Staying in builder land too long. Pilots that impress ten users rarely scale to one thousand without major rework. Shadow IT often creeps in as different teams experiment with their own builders, creating silos of half-working agents.

Agentic AI frameworks

An agentic AI framework is a platform built for enterprise adoption. These systems are designed with scalability, governance, and integration at their core. Instead of just producing a working prototype, frameworks provide the infrastructure needed to deploy, monitor, and secure agents across multiple teams, business units, and environments.

Where builders focus on quick wins, frameworks focus on reliable scale.

Strengths

• Scalability. Handle thousands of agents running across global regions.

• Security and compliance. Enforce policies, manage access, and satisfy regulations like GDPR, HIPAA, and SOC 2.

• Observability. Monitor performance, track ROI, and troubleshoot failures.

• Integration depth. Connect to SaaS platforms, APIs, data lakes, and even legacy systems.

• Deployment flexibility. Run in hybrid, on-premises, or multi-cloud environments.

Limitations

• Steeper learning curve for teams coming from builder toolkits

• Requires more upfront infrastructure investment in Kubernetes clusters, observability, and policy frameworks

Example

A global bank deploys an agentic platform to automate fraud detection across multiple geographies. The agents need to integrate with diverse payment systems, enforce financial regulations in every jurisdiction, and scale in real time. A builder cannot handle that scope. A framework is the only viable option.

Pitfall to avoid: Choosing a framework without Kubernetes-native support. If you cannot scale across multi-cluster AI workloads, you will hit performance and reliability ceilings as your agents grow.

The takeaway

• Builders are great for experimentation and validation.

• Frameworks are required for production and scale.

• The most successful enterprises use both — starting with builders to test ideas, then migrating viable use cases into frameworks that can support compliance, scale, and governance.

Comparison: builders vs frameworks

Choosing the right approach

If you need quick validation, start with a builder. If you need durable outcomes across thousands of users and workflows, you want a true agentic AI framework. Production requires orchestration, monitoring, governance, and secure multi environment deployment.

How enterprises benefit from an agentic framework

A strong agentic AI framework pays off across operations, cost, and risk. Here are the typical benefits.

Increased productivity

Agents take repetitive workflows like IT requests, procurement approvals, and document processing. Teams get hours back to focus on higher value work. Deloitte has reported intelligent automation productivity gains up to 30 percent. Enterprises that adopt agents at scale often see headcount efficiency improve within the first year.

Cost efficiency

Automation reduces manual labor and broken handoffs. McKinsey has estimated AI enabled automation could remove trillions in operational waste worldwide. In practice, that means fewer escalations, lower call center costs, and less time wasted waiting for approvals.

Flexibility across environments

Run agents in the cloud, on premises, or both. This supports customer facing assistants, back office orchestration, and advanced reasoning agents without forcing a single runtime. That flexibility is critical for hybrid enterprises.

Security and compliance

Policy enforcement, encryption, access controls, and audit trails are required for adoption in regulated industries. Frameworks that build this in allow teams to satisfy GDPR, HIPAA, and SOC 2 without bolting on compliance late.

Scalability

Once the foundation is in, you can scale across regions and lines of business. Hundreds of agents serving millions of interactions becomes a repeatable playbook.

Agentic AI applications: real use cases

Concrete use cases prove the point. These are the places where agentic AI applications already deliver results.

Customer support automation

Agents deflect Tier 1 and Tier 2 questions like password resets, order status, and account verification. Complex cases escalate to humans.

KPIs: 30 to 50 percent ticket deflection, handle time down 20 percent, CSAT up 5 points.

Example: A telco reduced call volume by nearly half by deploying customer-facing agents during peak periods.

DevOps and infrastructure management

Agents act like smart traffic controllers. They detect incidents, reroute workloads, apply patches, and scale clusters that run AI on Kubernetes before users feel pain.

KPIs: MTTR down 25 to 40 percent, false alert volume reduced by 20 percent.

Example: An e-commerce company used agents to rebalance workloads during holiday sales, avoiding outages during their biggest revenue days.

Data pipeline automation

Agents validate inputs, catch anomalies, retry failed steps, and notify owners with context.

KPIs: Failed jobs retried automatically 80 percent of the time, missed SLA runs cut by 60 percent.

Example: A global retailer ensures petabyte-scale nightly batch jobs complete on time, keeping dashboards current every morning.

Transaction security

Agents score transactions, compare them to history, apply policy, and escalate exceptions.

KPIs: Time to decision cut in half, false positives down 10 to 20 percent, full audit coverage.

Example: A bank deployed agents to monitor real-time payments, approving safe transactions instantly and flagging riskier ones for compliance officers.

Sales and contract assistance

Agents propose pricing within guardrails, draft terms, generate quotes, and route closed contracts to billing.

KPIs: Quote cycle time down 40 percent, legal review time cut 20 percent, win rate up by 3 to 5 points.

Example: A SaaS company accelerated deal closure by having agents prepare contracts overnight so sales teams woke up with ready-to-send documents.

Takeaway: Agents are digital coworkers who never sleep. They monitor, resolve, and keep operations moving so people can focus on strategy and creativity.

Key components of an AI agent framework

A true AI agent framework is an ecosystem. Each part plays a role. Together they make agents powerful and safe.

Orchestration engine

Coordinates workflows, handles dependencies, schedules execution, and prioritizes critical paths.

Example: A supply chain system verifies stock levels before triggering reorders and routes orders to the right warehouse.

Agentic RAG

Moves beyond retrieval to planning and reasoning.

Example: In healthcare, an agent pulls chart history, compares treatment guidelines, and drafts recommended care paths.

Integration layer

Connects agents to SaaS, APIs, data lakes, and legacy systems.

Example: A financial institution’s agent pulls CRM data, checks compliance, and processes a loan end to end.

Policy and governance controls

Defines guardrails, redaction, approvals, and auditability.

Example: A telecom provider proves GDPR compliance with complete logs for every agent action.

Monitoring and observability

Tracks latency, error rates, throughput, cost, and ROI.

Example: An e-commerce checkout agent scales capacity before users abandon carts due to latency.

Why it matters: Without orchestration, agents collide. Without reasoning, they act blind. Without integration, they get stuck. Without governance, they create risk. Without monitoring, they drift.

Typical challenges in agentic development

Enterprises must plan for common challenges.

1. Integration with legacy systems. Old platforms lack APIs. Agents must bridge gaps without breaking workflows.

2. Balancing compliance and performance. Speed cannot override regulatory rules. Audit trails are as important as low latency.

3. Managing costs at scale. GPU burn and data transfer add up. Without cost visibility, ROI disappears.

4. Ensuring reliability across APIs and tools. Every dependency is a failure point. Build graceful fallbacks.

5. Handling exceptions and edge cases. Escalation paths matter. Human oversight catches what automation cannot.

If ignored, these turn agentic AI into liability. A strong agentic AI framework provides orchestration, compliance, monitoring, and scalability to overcome them.

Comparing top agentic AI frameworks

Here is a snapshot of leading options. Fit depends on your workload and scale.

Key takeaway: There is no one-size-fits-all. Prototype with developer tools. Deploy at scale with enterprise-grade frameworks.

How to choose the best solution

Pick a framework like you pick a vehicle. A bike is fine for short commutes. A plane is needed for continents. Your scale and compliance needs drive the choice.

1. Identify workloads. Define clear use cases. Insurance may target claims automation. Telco may target network incidents.

2. Check Kubernetes-native support. Ensure the framework scales on multi-cluster AI workloads.

3. Governance requirements. Look for built-in policy enforcement and audit trails.

4. Vendor neutrality. Favor open source AI agents to reduce lock in.

5. Experimentation vs production. Prototype with LangChain. Scale on Mirantis k0rdent AI when you need enterprise-grade controls.

Open source AI agents vs commercial platforms

You can build with open source, purchase proprietary software, or mix both. Each path has trade-offs.

Open source AI agents

• Flexibility to customize agents for unique workloads.

• Lower licensing cost.

• Community innovation from global developers.

• Reduced lock in.

Example: A logistics company uses LlamaIndex to adapt quickly to supply chain changes.

Commercial agentic AI software

• Managed services with integrations and support.

• Enterprise-grade security certifications.

• Faster time to value for lean teams.

Example: A financial services firm selects a commercial platform with compliance certifications to accelerate adoption.

Key trade off: Open source maximizes agility. Commercial maximizes convenience. Many enterprises blend both.

How to build custom AI agents for your enterprise

This is where many guides go light. Here’s the full playbook.

Step 1: Define the use case

• Pick measurable goals: 50 percent faster onboarding, 30 percent fewer escalations, or 40 percent ticket deflection.

• Start where pain is visible and payoff is fast.

• Avoid critical systems for pilots.

Example: An insurer automates simple claims intake first.

Pitfall: Fuzzy goals. If you cannot measure, you cannot prove value.

Step 2: Select the framework

• Evaluate scalability.

• Demand Kubernetes-native deployment for AI on Kubernetes.

• Check compliance controls.

Example: A telco moves from LangChain to Mirantis k0rdent AI to handle global rollout.

Pitfall: Choosing tools that break once workloads hit scale.

Step 3: Build the agent

• Define logic: what the agent decides and what workflows it follows.

• Connect APIs, databases, and SaaS.

• Design human-in-the-loop steps.

Example: A healthcare agent drafts care paths but requires physician signoff.

Pitfall: Over-automation. Some approvals need people.

Step 4: Test and validate

• Reliability under load.

• Compliance with audit trails.

• ROI vs compute cost.

Example: A retailer simulates peak load before holiday season.

Pitfall: Pilots without exit criteria. They drift until they die.

Step 5: Deploy and scale

• Roll out gradually by business unit.

• Instrument everything.

• Standardize with Mirantis k0rdent AI for observability and governance.

Example: A bank expands agents region by region, scaling safely with Kubernetes-native clusters.

Pitfall: Big bang rollouts that collapse under pressure.

Best practices for agentic AI development

These principles keep projects on track.

• Start small, scale fast.

• Prioritize governance from day one.

• Optimize for AI on Kubernetes to ensure portability.

• Measure outcomes, not activity.

• Instrument for cost, latency, and ROI.

• Keep iterating based on real-world feedback.

Treat it like launching an airline. Start with one route. Nail safety. Expand once reliable.

Streamline the development of agentic AI with Mirantis

Enterprises do not need another experiment. They need production. Mirantis helps teams deploy agentic AI platforms that handle real workloads, governance, and compliance. We built for modern infrastructure challenges so you can focus on outcomes.

With Mirantis k0rdent AI, organizations can:

• Run multi-cluster AI workloads across hybrid and cloud-native environments

• Enforce enterprise-grade security and compliance

• Integrate with existing pipelines and SaaS tools to maximize ROI

• Avoid lock in with flexible architecture designed for AI factories

Mirantis helps you move beyond pilots into production at scale.

Agentic AI Frameworks: Building Custom Agents is not a future concept. It is a playbook ready to execute. Book a demo and see how Mirantis k0rdent AI turns your use cases into live agents that deliver results.