Building a Genesys Cloud Agent Assist Segment Highlighter with Python

What You Will Build

A Python service that pushes precise transcript highlights to active Genesys Cloud conversations using the Agent Assist API, validates annotation density, resolves character overlaps, and streams metrics to external training modules. This implementation uses the genesyscloud Python SDK, real-time WebSocket transcript streaming, and deterministic offset calculation to guarantee UI compatibility and agent focus alignment. The code covers Python 3.9+.

Prerequisites

• Genesys Cloud OAuth service account with agentassist:annotation:write, agentassist:annotation:read, conversation:stream:read, and webhook:read scopes
• genesyscloud Python SDK v2.10+
• httpx>=0.24.0, websockets>=12.0, pydantic>=2.0
• Python 3.9 runtime
• External training module endpoint accepting POST JSON payloads for webhook synchronization

Authentication Setup

Genesys Cloud OAuth requires a client credentials flow for server-to-server operations. The following code caches the access token and handles automatic expiration detection.

import os
import time
import httpx
from typing import Optional

class GenesysAuthManager:
    def __init__(self, client_id: str, client_secret: str, region: str = "mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.auth_url = f"https://api.{region}/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0.0

    def get_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry - 30:
            return self.access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        
        response = httpx.post(self.auth_url, data=payload, timeout=15.0)
        response.raise_for_status()
        
        token_data = response.json()
        self.access_token = token_data["access_token"]
        self.token_expiry = time.time() + token_data["expires_in"]
        return self.access_token

Implementation

Step 1: Payload Construction with Span Matrix and Mark Directive

The Agent Assist UI enforces strict rendering limits. You must construct a deterministic span matrix that maps segment identifiers to character ranges, and attach a mark directive that tells the client how to render the highlight.

from pydantic import BaseModel, Field
from typing import List, Dict

class SpanEntry(BaseModel):
    segment_id: str
    start_offset: int = Field(ge=0)
    end_offset: int = Field(gt=0)
    text: str

class MarkDirective(BaseModel):
    type: str = "highlight"
    color: str = "bg-yellow-200"
    focus_agent: bool = False
    priority: int = Field(ge=1, le=5)

class HighlightPayload(BaseModel):
    conversation_id: str
    segment_id: str
    start_offset: int
    end_offset: int
    text: str
    metadata: Dict[str, object]
    mark_directive: MarkDirective

    def to_genesys_body(self) -> Dict[str, object]:
        return {
            "conversationId": self.conversation_id,
            "segmentId": self.segment_id,
            "startOffset": self.start_offset,
            "endOffset": self.end_offset,
            "text": self.text,
            "type": self.mark_directive.type,
            "metadata": {
                "markDirective": self.mark_directive.model_dump(),
                "focusTrigger": self.mark_directive.focus_agent,
                "renderPriority": self.mark_directive.priority
            }
        }

def build_span_matrix(transcript: str, target_phrases: List[str]) -> List[SpanEntry]:
    matrix = []
    for phrase in target_phrases:
        idx = transcript.lower().find(phrase.lower())
        if idx != -1:
            matrix.append(SpanEntry(
                segment_id="current_segment",
                start_offset=idx,
                end_offset=idx + len(phrase),
                text=transcript[idx:idx + len(phrase)]
            ))
    return matrix

Step 2: UI Constraint Validation and Overlap Resolution

The Genesys Cloud agent desktop caps annotation density to prevent visual fatigue. You must validate against maximum highlight counts per segment, enforce character length limits, and resolve overlapping spans deterministically.

MAX_HIGHLIGHTS_PER_SEGMENT = 5
MAX_HIGHLIGHT_CHAR_LENGTH = 150
SENSITIVE_PATTERNS = [r"\b\d{4}(\s|\-)?\d{4}(\s|\-)?\d{4}(\s|\-)?\d{4}\b", r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b"]

def validate_highlight_density(existing_spans: List[SpanEntry], new_span: SpanEntry) -> bool:
    if len(existing_spans) >= MAX_HIGHLIGHTS_PER_SEGMENT:
        return False
    if len(new_span.text) > MAX_HIGHLIGHT_CHAR_LENGTH:
        return False
    for existing in existing_spans:
        if existing.segment_id == new_span.segment_id:
            if not (new_span.end_offset <= existing.start_offset or new_span.start_offset >= existing.end_offset):
                return False
    return True

def filter_sensitive_content(text: str) -> str:
    import re
    for pattern in SENSITIVE_PATTERNS:
        text = re.sub(pattern, "[REDACTED]", text, flags=re.IGNORECASE)
    return text

def calculate_readability_score(text: str) -> float:
    words = text.split()
    sentences = max(1, len([c for c in text if c in ".!?"]))
    syllables = sum(max(1, len(word.replace("y", "").lower()) // 2) for word in words)
    if len(words) == 0:
        return 100.0
    return 206.835 - 1.015 * (len(words) / sentences) - 84.6 * (syllables / len(words))

Step 3: Atomic WebSocket Text Operations and Agent Focus Triggers

Real-time transcript updates arrive via WebSocket. You must process text deltas atomically to maintain accurate character offsets across concurrent agent interactions. The following code maintains a thread-safe transcript buffer and triggers automatic agent focus when high-priority marks are applied.

import asyncio
import threading
import websockets
from collections import deque

class TranscriptBuffer:
    def __init__(self):
        self.lock = threading.Lock()
        self.segments: Dict[str, str] = {}
        self.offset_queue: deque = deque()

    def append_segment(self, segment_id: str, text_delta: str):
        with self.lock:
            if segment_id not in self.segments:
                self.segments[segment_id] = ""
            self.segments[segment_id] += text_delta
            self.offset_queue.append((segment_id, len(self.segments[segment_id])))

    def get_segment(self, segment_id: str) -> str:
        with self.lock:
            return self.segments.get(segment_id, "")

async def stream_transcript(conversation_id: str, access_token: str, region: str, buffer: TranscriptBuffer):
    ws_url = f"wss://api.{region}/api/v2/conversations/{conversation_id}/stream"
    headers = {"Authorization": f"Bearer {access_token}"}
    
    async with websockets.connect(ws_url, extra_headers=headers) as ws:
        async for message in ws:
            import json
            try:
                event = json.loads(message)
                if event.get("type") == "text":
                    seg_id = event.get("segmentId", "current")
                    text = event.get("text", "")
                    buffer.append_segment(seg_id, text)
            except json.JSONDecodeError:
                continue

Step 4: Highlight Validation Pipeline and External Webhook Synchronization

Before pushing to the Agent Assist API, run the payload through the sensitive phrase filter and readability pipeline. Successful highlights trigger a synchronization event to external training modules via HTTP POST.

import logging

logger = logging.getLogger("agentassist.highlighter")

def run_validation_pipeline(span: SpanEntry, existing_spans: List[SpanEntry]) -> tuple[bool, str]:
    cleaned_text = filter_sensitive_content(span.text)
    if cleaned_text != span.text:
        return False, "SENSITIVE_CONTENT_DETECTED"
    
    readability = calculate_readability_score(cleaned_text)
    if readability < 40.0:
        return False, "READABILITY_THRESHOLD_FAILED"
        
    if not validate_highlight_density(existing_spans, span):
        return False, "DENSITY_OR_OVERLAP_VIOLATION"
        
    return True, "VALID"

async def sync_training_webhook(payload: HighlightPayload, external_url: str, success: bool):
    webhook_body = {
        "event": "segment_highlighted",
        "conversation_id": payload.conversation_id,
        "segment_id": payload.segment_id,
        "highlight_success": success,
        "focus_triggered": payload.mark_directive.focus_agent,
        "metadata": payload.metadata
    }
    try:
        async with httpx.AsyncClient(timeout=10.0) as client:
            resp = await client.post(external_url, json=webhook_body, headers={"Content-Type": "application/json"})
            resp.raise_for_status()
    except httpx.HTTPStatusError as e:
        logger.error("Webhook sync failed: %s", e.response.status_code)

Step 5: Metrics Tracking, Audit Logging, and Retry Logic

Production highlighters must track latency, success rates, and maintain immutable audit logs. The following implementation wraps the SDK call with exponential backoff for 429 responses and records deterministic audit entries.

import time
from genesyscloud.platform_client_v2 import PureCloudPlatformClientV2
from genesyscloud.agentassist import AgentassistClient

class HighlightMetrics:
    def __init__(self):
        self.total_attempts = 0
        self.successful_marks = 0
        self.latencies: list[float] = []

    def record_attempt(self, latency: float, success: bool):
        self.total_attempts += 1
        self.latencies.append(latency)
        if success:
            self.successful_marks += 1

    def get_success_rate(self) -> float:
        return (self.successful_marks / self.total_attempts * 100) if self.total_attempts > 0 else 0.0

async def push_highlight_with_retry(client: AgentassistClient, payload: HighlightPayload, max_retries: int = 3) -> bool:
    body = payload.to_genesys_body()
    attempt = 0
    last_exception = None
    
    while attempt < max_retries:
        start_time = time.perf_counter()
        try:
            response = client.post_agentassist_annotations(body=body)
            latency = time.perf_counter() - start_time
            logger.info("Highlight pushed successfully. Latency: %.3fms", latency * 1000)
            return True
        except Exception as e:
            last_exception = e
            status_code = getattr(e, 'status_code', 0)
            if status_code == 429:
                wait_time = (2 ** attempt) + (hash(str(e)) % 1000) / 1000.0
                logger.warning("Rate limited. Retrying in %.2fs", wait_time)
                await asyncio.sleep(wait_time)
                attempt += 1
            else:
                logger.error("Highlight push failed: %s", str(e))
                break
    return False

Complete Working Example

import asyncio
import logging
import os
from typing import Dict, List

logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s")
logger = logging.getLogger("agentassist.highlighter")

# Import classes from previous steps
# (GenesysAuthManager, SpanEntry, MarkDirective, HighlightPayload, TranscriptBuffer, HighlightMetrics)
# For brevity in this script, assume they are defined in the same file or imported.

async def run_highlighter_pipeline(
    client_id: str,
    client_secret: str,
    region: str,
    conversation_id: str,
    target_phrases: List[str],
    external_webhook_url: str
):
    auth = GenesysAuthManager(client_id, client_secret, region)
    token = auth.get_token()
    
    client = PureCloudPlatformClientV2()
    client.set_access_token(token)
    agentassist_client = client.agentassist_client
    
    buffer = TranscriptBuffer()
    metrics = HighlightMetrics()
    existing_spans: List[SpanEntry] = []
    
    task = asyncio.create_task(stream_transcript(conversation_id, token, region, buffer))
    
    await asyncio.sleep(2.0)
    
    try:
        for phrase in target_phrases:
            segment_text = buffer.get_segment("current_segment")
            if not segment_text:
                continue
                
            matrix = build_span_matrix(segment_text, [phrase])
            if not matrix:
                continue
                
            span = matrix[0]
            valid, reason = run_validation_pipeline(span, existing_spans)
            if not valid:
                logger.info("Skipped phrase '%s': %s", phrase, reason)
                continue
                
            directive = MarkDirective(focus_agent=True, priority=5)
            payload = HighlightPayload(
                conversation_id=conversation_id,
                segment_id=span.segment_id,
                start_offset=span.start_offset,
                end_offset=span.end_offset,
                text=span.text,
                metadata={},
                mark_directive=directive
            )
            
            success = await push_highlight_with_retry(agentassist_client, payload)
            metrics.record_attempt(time.perf_counter() - time.perf_counter(), success)
            
            if success:
                existing_spans.append(span)
                await sync_training_webhook(payload, external_webhook_url, True)
                logger.info("Audit: Highlight applied | Segment: %s | Offset: %d-%d", span.segment_id, span.start_offset, span.end_offset)
            else:
                await sync_training_webhook(payload, external_webhook_url, False)
                
    except Exception as e:
        logger.error("Pipeline interrupted: %s", e)
    finally:
        task.cancel()
        try:
            await task
        except asyncio.CancelledError:
            pass
            
        logger.info("Session complete. Success rate: %.2f%% | Avg latency: %.3fms", 
                    metrics.get_success_rate(), 
                    sum(metrics.latencies)/len(metrics.latencies) if metrics.latencies else 0)

if __name__ == "__main__":
    asyncio.run(run_highlighter_pipeline(
        client_id=os.getenv("GENESYS_CLIENT_ID"),
        client_secret=os.getenv("GENESYS_CLIENT_SECRET"),
        region="mypurecloud.com",
        conversation_id="YOUR_CONVERSATION_ID",
        target_phrases=["escalation", "refund policy", "technical support"],
        external_webhook_url="https://training.example.com/api/v1/highlight-sync"
    ))

Common Errors & Debugging

Error: 400 Bad Request (Invalid Offset or Segment Reference)

• Cause: The startOffset or endOffset exceeds the actual transcript length, or segmentId does not match an active conversation segment.
• Fix: Verify character boundaries against the WebSocket transcript buffer before construction. Ensure endOffset is strictly greater than startOffset.
• Code Fix:

if span.start_offset >= len(segment_text) or span.end_offset > len(segment_text):
    logger.warning("Offset out of bounds. Adjusting to segment length.")
    span.end_offset = len(segment_text)

Error: 403 Forbidden (Missing OAuth Scope)

• Cause: The service account lacks agentassist:annotation:write.
• Fix: Navigate to Genesys Cloud Admin > Security > OAuth > Client Credentials > Scopes. Add the required scope and regenerate the token.
• Code Fix: Explicitly log the scope requirement during initialization:

REQUIRED_SCOPES = ["agentassist:annotation:write", "conversation:stream:read"]
logger.info("Required scopes: %s", REQUIRED_SCOPES)

Error: 429 Too Many Requests (Rate Limit Cascade)

• Cause: Exceeding the Genesys Cloud API rate limit for annotation creation or WebSocket message throughput.
• Fix: Implement exponential backoff with jitter. The push_highlight_with_retry function already handles this. Ensure you batch highlights if processing bulk transcripts.
• Code Fix: The retry logic in Step 5 uses 2^attempt + jitter. Increase max_retries to 5 for high-volume environments.

Error: WebSocket Disconnection (1006 Abnormal Closure)

• Cause: Network instability or idle timeout on the conversation stream.
• Fix: Implement a heartbeat ping/pong mechanism and automatic reconnection logic.
• Code Fix:

async def stream_transcript_resilient(...):
    while True:
        try:
            await stream_transcript(conversation_id, access_token, region, buffer)
        except websockets.ConnectionClosed:
            logger.warning("WebSocket closed. Reconnecting in 5s...")
            await asyncio.sleep(5)

Official References

• Genesys Cloud Agent Assist API Documentation
• Genesys Cloud Python SDK Reference
• Genesys Cloud WebSocket Streaming Guide
• Genesys Cloud OAuth Client Credentials Flow