Real-time Clinical Audio Processing with Agentic Workflows

abridge · Healthcare · 2026 · source Agentic: yes · promotional bias: low · source-backed · extracted with prompt 0.5.6 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Real-time clinical audio processing and healthcare agent workflows

Abridge needed to process clinician-patient conversations in real time, preserve protected health information durably, and decide during an encounter when clinical products should run.

Constraints:

• Clinical audio is high-stakes healthcare data with PHI, so losing audio or workflow state is unacceptable.
• New active-listening products needed responses within about five seconds after relevant speech.
• Streaming audio required high-throughput infrastructure that Temporal was not designed to carry directly.
• The system had to stay observable and durable across hundreds of healthcare systems, workflows, and EHR integrations.

Architecture

Abridge models each clinical conversation as a Temporal encounter workflow, while a separate Kafka/Flink streaming pipeline handles 500-millisecond audio chunks and transcription. Transcribed utterances are sent as Temporal signals into an agentic active-listening workflow. The agent accumulates transcript state, uses enriched patient/clinician context, decides when products should run, and triggers activities or child workflows while Temporal provides durability, memory, retries, and observability.

Diagram confidence: explicit (topology described by the source).

flowchart LR
  A["Clinical encounter recording"] --> B["Audio ingestion backend"]
  A --> G["Encounter workflow"]
  B --> C["Kafka audio topic"]
  C --> D["Flink stream processor"]
  D --> E["Kafka transcript topic"]
  E --> F["Transcript signal consumer"]
  F --> H["Agentic active-listening workflow"]
  G --> H
  G --> K["Nexus workflow calls"]
  H --> I["Clinical product activities"]
  H --> J["Clinician chat agent"]
  H --> L["OpenAI token streaming workflow"]

Components

Data flow

The clinician records an encounter. Audio enters Abridge's backend and is transcribed. For active listening, 500-millisecond audio chunks go to Kafka, Flink windows and transcribes them, transcribed words are emitted to another Kafka topic, and a consumer signals the Temporal active-listening workflow. Inside the workflow, an agent evaluates the accumulated transcript and context, then runs products as activities or child workflows.

Why this architecture

Abridge split responsibilities by system fit: Temporal handles durable, observable long-running workflows and agent state, while Kafka/Flink handle high-throughput audio streaming. This avoids forcing high-volume streaming through Temporal while still letting transcribed utterances trigger durable agentic logic.

Models used: OpenAI

Scale

• Abridge is deployed across hundreds of healthcare systems and providers.
  “we're also deployed across hundreds of healthcare systems and providers”transcript, stated
• Abridge integrates with several electronic health record systems.
  “we integrate with several electronic health record systems.”transcript, stated
• Abridge reports billions of actions per month and hundreds of workflows.
  “we have billions of actions per month. We have hundreds of workflows and we're just growing more and more.”transcript, stated
• The active-listening requirement is to return to the client within five seconds after relevant speech.
  “once something is said, to return back to the client within five seconds.”transcript, stated
• Streaming audio moved from 30-second chunks to 500-millisecond chunks.
  “instead of 30 second audio chunks, we ingest 500 millisecond audio chunks.”transcript, stated

Alternatives considered (roads not taken)

Synchronous microservice chains

Rejected because: They were hard to observe, fragile under failures, and difficult to extend without timing/race problems.

Queue/Kafka-only durable pipelines

Rejected because: Queues improved durability but worsened observability and introduced poison-record retry problems.

One-workflow-to-one-activity Temporal wrappers

Rejected because: That anti-pattern recreated synchronous microservices and failed to capture workflow-orchestration benefits.

Use Temporal for high-throughput streaming audio

Rejected because: The team found Temporal was not a good fit for moving large volumes of streaming data at high throughput.

Patterns

durable-execution (reliability)

Temporal encounter and active-listening workflows preserve state, retries, and observability for clinical audio and product execution.

agent-memory (agent-state)

Workflow state gives the active-listening agent short-term memory about what products have already run.

tool-use-orchestration (orchestration)

The active-listening agent triggers products as activities or child workflows when conversation state warrants it.

multi-agent-orchestration (orchestration)

Abridge envisions multiple workflow-hosted agents communicating via Temporal signals and queries.

observability-tracing (operations)

Temporal UI makes workflow failures and slow activities visible without digging through distributed logs and traces.

Tradeoffs

Use a hybrid Temporal plus Kafka/Flink architecture.

Chose Temporal for agent workflows and Kafka/Flink for streaming audio over A single orchestration system for all data movement.

Rationale: Temporal fits durable logic and stateful agent decisions; Kafka/Flink fit low-latency high-throughput audio streams.

Consolidate encounter processing into cohesive workflows.

Chose Single encounter workflow abstraction over Distributed microservice/workflow chains with HTTP hops.

Rationale: The encounter workflow makes lifecycle state and failures visible while reducing retry behavior across namespaces.

Use Temporal-based token micro-batching instead of true end-to-end streaming.

Chose Workflow signals, micro-batches, and API polling over Direct true streaming across regions/namespaces.

Rationale: The approach is not true streaming but feels responsive enough and fits Abridge's multi-region architecture.

Gotchas (what bit them)

Wrapping every operation as one workflow with one activity misses the point of workflow orchestration.

Root cause: The initial Temporal usage mirrored synchronous microservice chains instead of modeling the encounter lifecycle.

Resolution: Abridge consolidated around a single encounter workflow and moved cross-namespace calls to Nexus.

HTTP servers between workflow namespaces caused retry storms during outages.

Root cause: Activity-level retries against a slow or failing HTTP intermediary spawned many workflows in the calling namespace.

Resolution: Nexus replaced the HTTP hop so the calling namespace handles retry logic through a workflow command.

Durable streaming audio is expensive and maintenance-heavy.

Root cause: Sub-five-second active listening required 500-millisecond chunks, Kafka, Flink, transcription windows, and workflow signaling.

Resolution: Use the streaming architecture only because durable audio and low latency are required; otherwise avoid it.

Evaluation

The talk evaluates architecture through operational requirements: durability for clinical audio, observability in Temporal UI, sub-five-second active-listening latency, and proof-of-concept behavior for a clinician to-do list.

Metrics: Audio durability · Workflow observability · Sub-five-second response to relevant utterances · Single-digit millisecond Kafka read/write behavior · Correct avoidance of duplicate product execution

Outcomes

• Abridge moved from workflow anti-patterns toward a consolidated encounter workflow.
• Nexus removed the HTTP server layer that had caused retry pain between namespaces.
• The active-listening system can detect clinical intents during the conversation and trigger products.
• The team built a proof-of-concept to-do list that adds and checks off blood-pressure tasks from conversation.
• The architecture supports real-time clinical audio processing with durable agent workflow state.

Abridge reports hundreds of healthcare systems/providers, several EHR integrations, billions of actions per month, hundreds of workflows, 30-second legacy chunks, 500-millisecond streaming chunks, and a five-second client-response requirement.