Today, AI agents are gradually becoming integrated with the software solutions that we see and use today. They include customer support and internal knowledge assistants, workflow automation, and enterprise copilots.
One of the first considerations that engineers have to take is whether they should develop their own AI agent solution or buy one.
This decision is not only dictated by costs; it is dictated by architecture, scalability, integration, security, and further maintenance.
Buying an AI Agent Platform
During the last few years, commercial AI platforms have significantly improved their performance. Now many commercial platforms have the built-in feature set which includes RAG, tool calling, workflow orchestration, authentication, analytics, and API integration.
For engineering teams, this can significantly accelerate development processes.
Buying an AI platform will be the right decision for you if:
You want rapid deployment
You need standard enterprise use cases
Your need managed infrastructure
You need the built-in security features
You need vendor support and updates
By buying an AI platform, your team will be able to spend much less time on developing basic AI infrastructure and focus on integrating AI into existing products and processes.
Building an AI Agent
Building allows full ownership of the application stack.
The custom AI agent could be developed with unique business logic, proprietary datasets, custom APIs, and specific workflows in mind.
It usually contains the following components:
Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG)
Vector databases
Embedding models
Function calling
Agent orchestration frameworks
Memory management
Authentication
Monitoring and observability
Although this strategy maximizes flexibility, it also carries certain engineering challenges.
Among others, engineers would be responsible for:
Prompt tuning
Model evaluation
Latency
Cost optimization
Hallucinations mitigation
Security
Scaling
Deployment
Building is preferred when the AI technology is a crucial product capability rather than a side one.
Key Technical Considerations
To decide whether building or subscribing is preferable, there are some questions that should be answered by engineering teams before taking any actions.
Integration complexity
Would the AI agent be integrated with CRMs, ERPs, custom APIs, databases, or proprietary systems?
Deep integration is usually achieved through custom solutions.
Security and compliance
Some industries work with sensitive information and require a high level of security and compliance.
Scalability
Is the platform able to scale up in case of growing load?
Why Hybrid Architectures Become Increasingly Popular
Indeed, organizations tend to embrace both approaches simultaneously.
For instance:
Use existing APIs for language models.
Create custom orchestrators.
Use proprietary business data through RAG.
Leverage internal applications via function calling.
Implement organization-specific workflows.
A hybrid approach enables engineers to benefit from ready-made AI solutions while having full control over components that will differentiate a business.
Think Beyond the First Release
Creating an AI agent is just a starting point.
The production-ready system needs:
Logging.
Monitoring.
Evaluation pipelines.
Cost accounting.
Prompt versioning.
Security audit.
Model updating.
Optimizing performance.
Operational aspects usually take much more engineering time than development itself.
Educational materials for those who are interested in enterprise AI architectures, intelligent automation, and AI implementation best practices can be found on CommCon AI platform: [https://commconai.com/]
Conclusion
There is no universal "to buy" or "to build" decision.
If speed and simplicity of deployment matter, buying ready-to-use platforms may speed up the process.
If AI is a differentiating factor, building custom solutions may bring more value.
For many engineering teams, the optimal architectural approach becomes the combination of two methods described above.
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