I've been exploring AutoGen's multi-agent framework to automate some of the more labor-intensive aspects of revenue operations, specifically the analysis of customer discovery calls and internal sales pipeline reviews. My initial goal was a fully autonomous system that could ingest a meeting recording, produce accurate minutes, extract action items and key objections, and push relevant data points into our CRM. The core analysis agent works remarkably well, but I've hit a persistent bottleneck in the pre-processing stage.
The workflow I've constructed is as follows:
* **Agent 1: Orchestrator/Manager** - Receives the raw audio file, coordinates the workflow.
* **Step A: Transcription & Diarization** - This is the problem step. I am currently using a separate, external service API to generate a transcript with speaker labels. This step is manual from an agentic perspective, as I have to pre-process the file before handing it off to the agent team.
* **Agent 2: Transcript Processor** - Receives the diarized transcript, structures it, and prepares a clean summary for the next agent.
* **Agent 3: Analysis & Intelligence Agent** - This is where AutoGen shines. This agent, given a specific prompt framework, extracts:
* A concise summary of the discussion.
* A numbered list of specific commitments or action items (tagged by who owns them).
* Key pain points or objections raised by the prospect.
* Sentiment cues regarding specific product capabilities.
* **Agent 4: CRM Integration Agent** - Takes the structured output from Agent 3 and formats API calls to create tasks, update opportunity notes, and flag competitive concerns.
The intelligence and coordination from Step A onward are impressive. The agents debate ambiguous points, reconcile extraction disagreements, and produce a consolidated, reliable output. However, the necessity of manually handling speaker diarization outside the agentic loop breaks the automation promise. It means the system cannot operate on a true "fire-and-forget" basis for a net-new recording.
My current hypothesis is that robust, real-time diarization is computationally expensive and context-heavy, making it a poor fit for the current LLM-centric agent model. It likely requires a specialized, fine-tuned model operating at a lower level than the conversational layer AutoGen agents excel at.
I'm curious if others in the community have tackled this specific pipeline issue. Have you found a way to wrap a diarization service (like Azure Speech, Deepgram, or a local VAD model) into a reliable agent that can be managed by the Orchestrator? Or is the consensus that this pre-processing step will, for the foreseeable future, remain a separate, external function that simply prepares the payload for the agentic workflow?
The implications for forecast accuracy and pipeline hygiene are significant if this can be solved. Automating the ingestion of customer call nuance directly into Salesforce fields would be a game-changer for us.
--JK
measure what matters
The external API is a security vector you haven't accounted for. You're pushing raw, likely sensitive, call audio to a third party before your agents even touch it.
Have you evaluated the data processing terms of that service? It's now part of your data pipeline.
Look at Whisper or a self-hosted model for transcription. Keep the diarization output internal. Your "autonomous" system shouldn't start with an ungoverned data exfiltration step.
Least privilege is not a suggestion.
Completely valid point on the security vector. I went through the same thought process last year. Even with a reputable provider, you're adding a third party's data retention and incident response policies into your threat model for every single call.
What made the decision for us was the consistency of the output. When we tested Whisper, we found the diarization was... let's call it "creative" for internal meetings with people talking over each other. An external, paid API was just more reliable for our use case.
So we had to choose: accept the external dependency and wrap it in heavy contractual safeguards (DPA, clear data deletion schedules), or own the entire pipeline internally and manage the accuracy trade-offs. We chose the former, but it's a real cost and compliance lift.
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That's a great point about the pipeline itself being a security vector. It's not just about the data terms, but about architectural trust.
You made me realize my own team had a blind spot there when we started. We were so focused on getting the workflow to run that we didn't map where the raw data actually traveled first. That external step becoming the default "source of truth" for the whole system is a real design smell.
Have you seen teams bake the data processing review into their CI/CD for these agent workflows? Like a check that flags any new external API call before deployment?
Trust the trial period.
Totally feel your pain on the manual pre-processing step. That's the exact kind of friction that kills the "autonomous" part of the dream.
I ran into this with a HubSpot-Pipedrive sync setup. The analysis agent was brilliant, but waiting for a clean, labeled transcript to feed it felt like building a self-driving car that needed you to push it out of the garage. Have you considered wrapping that external API call into its own simple, single-purpose agent? It wouldn't solve the security concerns others raised, but it would at least make the entire chain *feel* agentic from ingestion onward. The orchestrator could pass the raw file to a "transcription agent" that handles the external call and formats the output for your processor agent.
Curious, are you having it push the extracted insights back into Salesforce? If so, how's the field mapping holding up when the diarization is a bit off?
Still looking for the perfect one
That's a clever idea, making the external call into its own single-purpose agent. It does seem like it would keep the workflow intact on paper, even if the dependency is still there.
You mentioned field mapping when diarization is off. In my tests with monday.com, even slight speaker mislabels cause a cascade of errors in the assigned action items. The agent can still extract the task, but it gets assigned to the wrong person's column. How do you handle that reconciliation?
That's a really interesting approach. I'm building something similar, just for internal support team meetings to auto-create help desk tickets. The whole "waiting for a clean transcript" part is what's slowing me down too.
If you turn that external API call into its own single-purpose agent, does it just make the pipeline longer without really fixing the core bottleneck? Like, you'd still be waiting on that external service's response time, right?
For the manual pre-processing, what if your orchestrator could at least trigger the external step automatically? It wouldn't be fully agentic, but maybe less manual?
Ask me in a year