Agentic AI voice systems are AI-powered tools that listen, understand, and take autonomous action based on spoken input. Unlike basic voice assistants, agentic AI voice technology reasons through problems, makes decisions, and executes multi-step workflows, all triggered by natural speech. 

The key to building effective agentic AI voice systems is high-quality transcription training data. Without accurate, well-annotated audio transcriptions, these autonomous systems cannot reliably interpret speech, detect intent, or make sound decisions. 

What Is Agentic AI? 

Agentic AI brings us closer to what most people think of as ‘AI’ than just a generative model that produces a single response based on a single input. 

Instead, using an Agent allows for a data pipeline that incorporates both generative AI and tools that plan ahead, reason through potential solutions, check output either alone or in combination with other agents, make decisions about what to do with the output, and carry out the multiple steps of a more complex workflow in a sensible, reliable way. 

Agentic AI is an extended use of generative AI, where models are augmented with ‘tools’. These tools are instructions that allow the model to make decisions that would otherwise require human intervention. 

These tools can be simple, collecting input and making predetermined decisions about what to do with each category of input, or they can be as complicated as your imagination allows: 

  • Updating databases 
  • Performing accurate calculations 
  • Verifying and validating data 
  • Ensuring tasks are completed to predefined specifications 

This all happens autonomously, without the need for human intervention during processing. 

Key Use Cases for Agentic AI Voice 

Call Centre Pre-Screening: Agentic AI pre-screens callers so they get correctly routed to the right consultant, along with necessary information such as the caller’s name, which is correctly spelt, the caller’s reference number, and an accurate description of the reason for the call. They can also include information to the consultant about the emotional state of the caller, if they sound stressed, harried, worried, or are neutral. 

Voice Assistants: Voice assistants powered by agentic AI respond more like humans, asking questions to fill in missing information, giving useful reports if something does not work, and offering suggestions on how to fix problems. 

Intelligent Search: Agentic AI understands anaphors and deictic words like ‘this’, ‘here’, ‘my’, and ‘now’. By having access to geolocation information, a local clock, and an awareness of the linguistic context, Agentic AI can generate accurate responses to natural language queries, including checking its own output before passing it on to the user. 

Education Coaches: Adaptive AI coaches accommodate a learner’s current understanding and tailor explanations and assistance to their level, supporting the learner’s cognitive development rather than replacing it. This could be a practice debate partner for ethics or philosophy classes, or patient conversational partners that provide language learners with more exposure to the target language, at a level appropriate to each particular learner. 

How Transcribed Audio Data Fits Into Agentic AI 

Agentic AI relies on accurate input to kick off its pipeline. For those tools that respond to voice, high-quality transcribed training data is critical to the success of Agentic AI. 

Good transcription training data does more than teach a model which words match which sounds. It can also teach the model: 

  • About different speakers 
  • How speakers signal turn-taking 
  • How speakers signal hesitancy or excitement or a lack of understanding 
  • Specific accents and voice qualities associated with different ages, genders, or moods 

Intent and Sentiment Classification 

Using an Agent, a transcription can be compared against a list of specific intents to determine the most likely intent given other contextual information. Example intents include: 

  • ‘Praise a product’ 
  • ‘Complain about a company’s customer service’ 
  • ‘Incite racial violence’ 
  • ‘Invite friend to birthday party on a particular date at a particular time’ 
  • ‘Remind receiver of medical appointment’ 

In a similar fashion, Agents can rank a transcribed text against different possible sentiments, such as ‘positive’, ‘negative’, ‘neutral’, even when a text contains both positive and negative sentiments. 

With training data that is also annotated for sentiment, transcription models can return more than just words, giving the language model, and thus the Agent, access to more human-like information to base its decisions on. This can include tone of voice, rate of speech, volume level, and other features that humans automatically extract from the spoken word. 

The Meeting Minutes Challenge 

LLMs on their own are not great at summarising meeting minutes nor at identifying action items arising during a meeting. But here is where Agentic AI can close the gap between current model and human capabilities. 

After being trained on meeting recordings and annotated transcriptions, an Agentic AI can step in to accurately identify action items identified during a meeting, by looking for trigger words in an automatic transcription, and ensuring that these are actually included in the generated meeting minutes. 

When a human provides a ‘summary’, we firstly evaluate our audience. We decide what they likely already know and the beliefs they likely hold that relate to our text. 

When creating meeting minutes, we focus on the action items arising from the meeting, because this is what we need in order to be able to move forward. We might add a ‘purpose’ of the meeting to the minutes, a list of attendees, and we choose which context to include based on our judgement of what is required to understand the action items, and possibly those items which will not be acted upon. 

Here is a critical example: If you record a meeting with two developers with the goal of working out a handover plan, but realise after twenty minutes of discussion that it makes more sense for the handover NOT to happen at this point, it is likely that the LLM will fail to notice that conclusion, since the bulk of the conversation is about something else. 

Training your model to pay attention to the conclusion, assign the correct sentiment, and understand the structure of your meeting in order to be able to take accurate meeting minutes or accurately assign sentiment and intent, requires training data that has accurate transcriptions, accurate speaker identification, accurately annotated speaker intent, and accurate sentiment labels. 

Agentic AI can improve the quality of a transcription by running specific checks over the text to ensure it has accurately identified the sentiment or intent of a particular text. 

When your training data contains a rich context, this knowledge is transferred into your tool. 

Quality vs. Quantity: What Matters More? 

It used to be thought quantity was better (cheaper) than quality. The rationale of ‘Big Data’ was that, even if there was noise, the actual patterns themselves would still be visible to the machine learning algorithm. There is still evidence that this is at least partially true. 

In a November 2024 paper entitled ‘Is Training Data Quality or Quantity More Impactful to Small Language Model Performance’, the authors report that ‘other research indicates that the sheer volume of training data remains crucial, particularly for ensuring comprehensive model coverage and minimizing overfitting’. 

However in the last couple of years, the biases and inconsistencies in ‘mediocre-quality’ datasets have shown themselves to be problematic, as people demand accuracy and factuality from AI. High data quality is now essential for compliance in regulated industries such as Finance and Retail. 

Leading thinkers in the field now prescribe ‘data hygiene’ as a facet of AI that must be embedded into the entire AI workflow, starting at data collection and including all agentic steps right through to the output. 

At a recent conference on generative AI held in Toronto, Divij Gupta, Applied AI Scientist at Homebase, noted that data hygiene must be embedded into the AI workflow (panel on ‘Overcoming challenges in risk, regulation & bias’ at AI Accelerator Institute’s Generative AI Summit in Toronto in November 2025). 

Why Quality Is Critical for Agentic AI 

Unlike generative AI, where both quality and quantity are important factors to consider during the collection of training data, when it comes to Agentic AI, quality is critical. 

This is because of the crucial difference between Agentic AI and Generative AI: Agents make decisions about ‘next steps’, and then carry out those next steps. Thus the onus on the training data to give the model a solid grounding is higher than when the output is a mostly direct-line from the input. 

Even when targeting a large volume of training data, the quality must be monitored in order to ensure it is not skewed or biased in undesirable ways. 

What Happens When Transcription Quality Is Poor? 

With low-quality training data, AI models are restricted in their capacity to draw correct, and useful, generalisations. Given predictions that up to 40% of AI projects may be cancelled by 2027, and that reliability is likely to be a major cause of project cancellations, it is clear that data quality is critical to the success of AI and Agentic AI projects. 

Poor-quality transcription data, like all low-quality data that is used to train any machine learning model, is difficult to find real patterns in. In particular, effects that may be small, but real, can be completely ignored, leading to these effects being effectively deleted from the model’s knowledge. 

The tools that an agent uses all depend on accurate input data to make their decisions: 

  • ‘Identify trigger words to route a call’ 
  • ‘Classify sentiment of speaker and apply a suitable emotional filter to the reply’ 
  • ‘Retrieve the requested information from the database’ 
  • ‘Calculate the current value of the user’s checkout items’ 

Model Drift and Cascading Errors 

One aspect of Agentic AI that makes it incredibly powerful is its ability to revise and update the data it uses when making decisions. If this is done well, the entire model will improve. But if there are errors, these can cause the model to ‘drift’ from its initial capabilities. 

Hallucinations and incorrect decisions erode user trust. 

The errors that an Agentic model makes can be subtle at first, before they begin to cascade. Your Agentic model might use its Transcription training data to correctly interpret caller requests, but then begin to route callers incorrectly, because they learn that after speaking with department A, the callers are regularly routed on to department B, and so the agent begins to route callers directly to department B, which may skip important steps in department A. 

The Dual Responsibility 

While Agents themselves must be built such that they do not deviate from the desired routing system, the original Transcription training data must also be as clear and complete as possible, in order that the model learns robustly in the first place. 

Simply adding more Transcription data in these instances opens up the possibility that undesirable generalisations are made, and thus the Agent will not behave reliably. 

This has been described as ‘the perfect storm: when poor data meets autonomous decision-making’, and is an excellent description of why you need to start your Agentic AI that relies on audio input, with excellent audio transcription training data. 

How to Identify High-Quality Transcription for Agentic AI 

High-quality transcriptions are not just about getting the words right. They accompany audio recordings of speakers with a specific range of accents and voice types, ensuring your model can correctly interpret these. 

Transcription for Agentic AI should also contain non-speech annotations that allow the model to learn richer and more robust patterns of communication, including intent, sentiment, and speaker attitudes. 

High-quality transcription data annotated with pragmatic and semantic information teaches your model to be socially and emotionally aware, making it easier for humans to interact with in a natural way. 

Diversity and Curation 

High-quality transcription data is diverse across many axes. It covers enough of a range of topics such that your model does not memorise specific details included in the training data, but instead learns the correct generalisations. 

But high-quality data need not cover every topic. Selecting a curated range of common topics that represent the real-world use cases, making sure to include a controlled set of edge cases, keeps the dataset smaller: focused, efficient, and cost-effective. 

Example: Collecting data to train a call centre agent. Data should include the frequent conversational patterns used in this type of conversation: 

  • Greeting and identification 
  • Authentication workflows 
  • Clarifying questions 
  • Problem descriptions 
  • Resolution steps 
  • Cues that indicate a call needs to be escalated 

It should also include variations such as: 

  • Different customer attitudes 
  • Examples of how to deal with incomplete or incorrect information 
  • Various real-life background noises 
  • Interruptions 

This intentional selection helps constrain dataset size without sacrificing performance. It ensures the model learns the structure, flow, and reasoning patterns of real interactions rather than memorising narrow examples. 

Technical Quality Standards 

High-quality transcription data for training Agentic AI is clean: 

  • Accurate timestamp placement 
  • Speakers are clearly identified 
  • Data is annotated with labels that match the features your Agents need to respond to and the capabilities your Agent needs to develop 
  • Spelling is standardised, including celebrity names, brand names, and other proper nouns, to ensure your model learns actual token patterns instead of making up patterns it finds in noise 

Clean transcription also includes well-defined conventions around punctuation, disfluencies, overlapping speech, and non-verbal sounds, ensuring that every element is represented in a predictable and machine-usable form. 

Why Clean Data Matters for Complex Agentic Systems 

For agentic systems that rely on reasoning, turn-taking, or context tracking, such as AI assistants that must follow multi-step instructions, negotiate with users, handle interruptions gracefully, or maintain long-range conversational memory, clean transcription becomes even more critical. 

These systems depend on: 

  • Precisely segmented dialogue turns 
  • Accurately attributed speakers 
  • Stable linguistic cues to infer intent, detect shifts in topic, and decide what action to take next 

Any ambiguity or inconsistency in the underlying transcripts can lead to breakdowns in reasoning chains, incorrect inferences, or poorly timed responses. 

Quality Certification 

Your transcription should come with a certificate of quality, assuring a maximum Word Error Rate and Tag Error Rate for the data, with 95% confidence and a small sampling error (1 or 2%). 

This gives you confidence that the dataset is not only clean but verifiably clean. 

How High-Quality Transcription Is Created 

High-quality transcription data is generated by humans. While using ASR to provide a first-pass transcription can be helpful, humans still have the edge over fully automatic transcriptions, as humans are able to use the broader context to infer meaning from mumbled or unclear speech. 

Even with overpowering background noise interference, humans can identify names of new people, places, and acronyms. 

What to Look for in a Transcription Provider 

High-quality transcription from a particular company is a repeatable feat. The databases are validated to a standard, such as a maximum of 5% of utterances having an error in either spelling or tags, and the unique words transcribed containing a maximum of 2% errors. 

You should look for a company that provides human transcription that is supported by human-driven automation. 

Simply automating fixes, such as using an existing spell-checker or automatically aligning timestamps with certain amplitudes, cannot solve the range of inconsistencies and errors that can be found in a transcription. 

Instead, humans with expertise in both the target language and the creation of their own, specific automations, will produce the cleanest transcription. 

“The key is matching the talent to the task.” – Lori Bieda, Chief Data & Analytics Officer at BMO Financial Group, in the session on ‘Monetizing Innovation and Mastering Change Management for Generative and Agentic AI’ at the AI Accelerator Institute Generative AI Summit in Toronto in November 2025 

Speech data presents high ambiguity and novel issues each time. Human judgement, combined with human-driven automation, is essential for achieving high quality transcriptions.