5 Steps to Ensuring a Smooth Knowledge Transfer for AI Agents

Tomas Gear
Parloa

In June, Gartner analysts sent shockwaves across the enterprise AI industry with their bold prediction that 40% of agentic AI projects will be cancelled by 2027. They cited the cost and complexities of deploying AI agents at scale as the primary reasons for this prediction. One of enterprise leaders' most complex challenges is preparing their knowledge base for use in an agentic AI project.

Integrating distributed knowledge, ensuring information accuracy, and architecting AI agents are not just technical exercises — they are strategic imperatives for any organization seeking to succeed with its agentic AI initiatives. Your company's knowledge base is the engine that will power your AI agents. It's the structured data repository that an AI system uses to understand, reason, and make decisions. The knowledge base will be the gray matter of your AI agents' brains. Thus, transferring accurate knowledge is crucial. Incomplete or poor-quality data can reduce agent performance, accuracy, and even increase agent bias.

Here are five steps to ensuring that your company's knowledge base transfer is optimal.

1. Consolidating organizational sprawl

The first, and often the most difficult, step is consolidating your company's knowledge into one unified, accurate, and accessible source. At the heart of any effective agentic AI system lies one asset: organizational knowledge. Yet, as enterprises have grown, so too has the sprawl of knowledge — scattered across departments, tools, and formats. Data sprawl creates a knowledge gap, because it is difficult to identify data location and ownership. When data is lost, it is often unrecoverable.

It's worth noting that this will not be a one-off job. When we're talking about customer service, for example, it's not about ingesting that information just once and then having it distributed to customers. It is especially relevant to keep it up to date and think of knowledge as something that needs to be regularly maintained.

Key Actions for Achieving Consolidation:

  • Aggregate and Validate: Use technologies you already have, such as Azure AI Search, to unify data. Ensure all data is verified and free of conflicts. Eliminate any outdated content.
  • Assign Ownership: Build a team of subject matter experts based on each person's domain strengths. Assign subject matter experts the responsibility for maintaining ongoing data accuracy. Knowledge must be continuously maintained, not simply imported.
  • Automate Where Possible: Leverage AI and automation tools to help automatically detect ambiguity, outdated data, duplicates, and discrepancies. In the end, humans should always make the final decision.

2. Integration: Eliminating Silos

The best knowledge is useless if it's trapped in data silos or lost in translation between disconnected systems. Technical integration is vital for agentic AI success. Leaders must focus on aggregation and real-time synchronization across all knowledge repositories, ensuring seamless interoperability with AI agents.

Key Actions for Achieving Integration:

  • Synchronize Changes in Real Time: Any updates to knowledge should be reflected instantly across all systems feeding the AI. creating a single source of truth.
  • Prepare for Multimodality: Enterprises must be able to handle diverse file types, such as text, images within PDFs, and even external references that affect the factual reliability of responses.
  • Design for Compatibility: Ensure integration mechanisms work for aggregation and active use by AI systems, reducing friction between legacy and modern platforms.

3. Mastering Data Retrieval and Application

Remember that agentic AI thrives not on the sheer volume of knowledge, but on the precision of its retrieval and application. Knowing which data to retrieve and how to use it will be the game-changer for your agentic AI project. This requires a clear strategy for domain separation — defining specific knowledge "buckets" — and robust quality assurance processes.

With agentic AI systems, it is better to split the domains into respective buckets. Define the different domains that need to be handled and make sure that specialists are available for respective knowledge retrieval. Proper quality assurance with, for example, human-in-the-loop approaches, is essential.

Key Actions to Achieve Precision Retrieval:

  • Define and Limit Scope: By narrowing knowledge domains, organizations make quality control manageable while improving retrieval accuracy.
  • Human Oversight: Subject matter experts should be involved in reviewing responses, especially when AI interacts with ambiguous or complex queries.
  • Smart Conversational Design: Build agentic systems that clarify user requests, routing queries to the correct knowledge base.

4. Focusing on Context Over "Training"

Contrary to popular belief, the primary challenge in deploying agentic AI is no longer traditional "training" of models. Instead, it is about providing the proper context, curated and orchestrated by a new breed of professionals: agent architects and AI prompt engineers.

Key Actions to Provide More Context:

  • Shift from ML Training to Context Engineering: It's less about fine-tuning models, and more about architecting the context and guidance that AI agents need to perform.
  • Acceleration of Context Engineering: As systems become more sophisticated, context engineering builds on OCR and ETL pipelines that allow LLMs to use RAG and Vector Search to access custom, live, and proprietary data.
  • Use Case Definition: Effective AI agents require clear use case data, drawn from real customer pain points — not misleading conversational analytics from outdated IVR systems.

5. Future-Proofing the Stack with Open-Standards Tech

Many enterprises are still boxed in by legacy infrastructure. To succeed with agentic AI, companies must invest in technical modernization. This means aligning with open standards such as Model Context Protocol (MCP), which enable agent-to-agent collaboration and cross-system compatibility.

Key Actions for Enterprises:

  • Assess and Modernize: Identify which legacy systems hinder integration and prioritize making them accessible.
  • Adopt Open Standards: Invest in emerging protocols that facilitate inter-agent communication, collaboration, and future expansion.

Conclusion

Gartner's prediction may seem discouraging for those leading an enterprise agentic AI project. However, with careful planning and thoughtful preparation of your company's knowledge base, you can sidestep the pitfalls that place other companies in that unfortunate 40%. It will require far more than adopting the latest AI model or platform. It demands a holistic strategy: consolidating and maintaining accurate knowledge, breaking down technical silos, orchestrating precise retrieval, and embracing the new disciplines of context and prompt engineering.

By acting now, leaders can ensure that tomorrow's AI agents not only deliver on their promise, but do so with the organization, precision, reliability, and agility that today's customers — and tomorrow's enterprises — will demand.

Tomas Gear leads the Agent Engineering Team at Parloa

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5 Steps to Ensuring a Smooth Knowledge Transfer for AI Agents

Tomas Gear
Parloa

In June, Gartner analysts sent shockwaves across the enterprise AI industry with their bold prediction that 40% of agentic AI projects will be cancelled by 2027. They cited the cost and complexities of deploying AI agents at scale as the primary reasons for this prediction. One of enterprise leaders' most complex challenges is preparing their knowledge base for use in an agentic AI project.

Integrating distributed knowledge, ensuring information accuracy, and architecting AI agents are not just technical exercises — they are strategic imperatives for any organization seeking to succeed with its agentic AI initiatives. Your company's knowledge base is the engine that will power your AI agents. It's the structured data repository that an AI system uses to understand, reason, and make decisions. The knowledge base will be the gray matter of your AI agents' brains. Thus, transferring accurate knowledge is crucial. Incomplete or poor-quality data can reduce agent performance, accuracy, and even increase agent bias.

Here are five steps to ensuring that your company's knowledge base transfer is optimal.

1. Consolidating organizational sprawl

The first, and often the most difficult, step is consolidating your company's knowledge into one unified, accurate, and accessible source. At the heart of any effective agentic AI system lies one asset: organizational knowledge. Yet, as enterprises have grown, so too has the sprawl of knowledge — scattered across departments, tools, and formats. Data sprawl creates a knowledge gap, because it is difficult to identify data location and ownership. When data is lost, it is often unrecoverable.

It's worth noting that this will not be a one-off job. When we're talking about customer service, for example, it's not about ingesting that information just once and then having it distributed to customers. It is especially relevant to keep it up to date and think of knowledge as something that needs to be regularly maintained.

Key Actions for Achieving Consolidation:

  • Aggregate and Validate: Use technologies you already have, such as Azure AI Search, to unify data. Ensure all data is verified and free of conflicts. Eliminate any outdated content.
  • Assign Ownership: Build a team of subject matter experts based on each person's domain strengths. Assign subject matter experts the responsibility for maintaining ongoing data accuracy. Knowledge must be continuously maintained, not simply imported.
  • Automate Where Possible: Leverage AI and automation tools to help automatically detect ambiguity, outdated data, duplicates, and discrepancies. In the end, humans should always make the final decision.

2. Integration: Eliminating Silos

The best knowledge is useless if it's trapped in data silos or lost in translation between disconnected systems. Technical integration is vital for agentic AI success. Leaders must focus on aggregation and real-time synchronization across all knowledge repositories, ensuring seamless interoperability with AI agents.

Key Actions for Achieving Integration:

  • Synchronize Changes in Real Time: Any updates to knowledge should be reflected instantly across all systems feeding the AI. creating a single source of truth.
  • Prepare for Multimodality: Enterprises must be able to handle diverse file types, such as text, images within PDFs, and even external references that affect the factual reliability of responses.
  • Design for Compatibility: Ensure integration mechanisms work for aggregation and active use by AI systems, reducing friction between legacy and modern platforms.

3. Mastering Data Retrieval and Application

Remember that agentic AI thrives not on the sheer volume of knowledge, but on the precision of its retrieval and application. Knowing which data to retrieve and how to use it will be the game-changer for your agentic AI project. This requires a clear strategy for domain separation — defining specific knowledge "buckets" — and robust quality assurance processes.

With agentic AI systems, it is better to split the domains into respective buckets. Define the different domains that need to be handled and make sure that specialists are available for respective knowledge retrieval. Proper quality assurance with, for example, human-in-the-loop approaches, is essential.

Key Actions to Achieve Precision Retrieval:

  • Define and Limit Scope: By narrowing knowledge domains, organizations make quality control manageable while improving retrieval accuracy.
  • Human Oversight: Subject matter experts should be involved in reviewing responses, especially when AI interacts with ambiguous or complex queries.
  • Smart Conversational Design: Build agentic systems that clarify user requests, routing queries to the correct knowledge base.

4. Focusing on Context Over "Training"

Contrary to popular belief, the primary challenge in deploying agentic AI is no longer traditional "training" of models. Instead, it is about providing the proper context, curated and orchestrated by a new breed of professionals: agent architects and AI prompt engineers.

Key Actions to Provide More Context:

  • Shift from ML Training to Context Engineering: It's less about fine-tuning models, and more about architecting the context and guidance that AI agents need to perform.
  • Acceleration of Context Engineering: As systems become more sophisticated, context engineering builds on OCR and ETL pipelines that allow LLMs to use RAG and Vector Search to access custom, live, and proprietary data.
  • Use Case Definition: Effective AI agents require clear use case data, drawn from real customer pain points — not misleading conversational analytics from outdated IVR systems.

5. Future-Proofing the Stack with Open-Standards Tech

Many enterprises are still boxed in by legacy infrastructure. To succeed with agentic AI, companies must invest in technical modernization. This means aligning with open standards such as Model Context Protocol (MCP), which enable agent-to-agent collaboration and cross-system compatibility.

Key Actions for Enterprises:

  • Assess and Modernize: Identify which legacy systems hinder integration and prioritize making them accessible.
  • Adopt Open Standards: Invest in emerging protocols that facilitate inter-agent communication, collaboration, and future expansion.

Conclusion

Gartner's prediction may seem discouraging for those leading an enterprise agentic AI project. However, with careful planning and thoughtful preparation of your company's knowledge base, you can sidestep the pitfalls that place other companies in that unfortunate 40%. It will require far more than adopting the latest AI model or platform. It demands a holistic strategy: consolidating and maintaining accurate knowledge, breaking down technical silos, orchestrating precise retrieval, and embracing the new disciplines of context and prompt engineering.

By acting now, leaders can ensure that tomorrow's AI agents not only deliver on their promise, but do so with the organization, precision, reliability, and agility that today's customers — and tomorrow's enterprises — will demand.

Tomas Gear leads the Agent Engineering Team at Parloa

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How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Why ITOps Need Right-Sized AI, Not Bigger Models

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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