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logging-and-monitoring-for-agentic-workflows Comprehensive observability patterns for GitHub Agentic Workflows including structured logging, metrics collection, alerting strategies, debugging techniques, and production monitoring best practices for autonomous agent systems.
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Comprehensive expertise in GitHub Agentic Workflows (v0.68.1) — AI-powered repository automation with five-layer security, safe outputs, MCP tools, and Continuous AI patterns
github-agentic-workflows-mcp-configuration Comprehensive guide for MCP (Model Context Protocol) server setup, transport protocols, configuration validation, lifecycle management, tool discovery, and error handling patterns
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name Logging and Monitoring for Agentic Workflows description Comprehensive observability patterns for GitHub Agentic Workflows including structured logging, metrics collection, alerting strategies, debugging techniques, and production monitoring best practices for autonomous agent systems. license Apache-2.0 version 2.0.0 last_updated 2026-04-02 tags ["logging","monitoring","observability","agentic-workflows","metrics","alerting","debugging","structured-logging","telemetry","tracing"]
📊 Logging and Monitoring for Agentic Workflows
🔴 AI FIRST Quality Principle
Apply the AI FIRST principle: never accept first-pass quality. Minimum 2 iterations. Read all output, improve every section. No shortcuts.
📋 Overview
This skill provides comprehensive patterns for implementing observability in GitHub Agentic Workflows. It covers structured logging architectures, metrics collection, alerting strategies, debugging techniques, and production monitoring best practices for autonomous agent systems.
🎯 Core Concepts
Observability Architecture graph TB
subgraph "Agent Execution"
A[Agent Start] --> B[Task Execution]
B --> C[MCP Operations]
C --> D[Agent Completion]
end
subgraph "Logging Layer"
B --> E[Structured Logs]
C --> E
D --> E
E --> F[Log Aggregation]
end
subgraph "Metrics Layer"
B --> G[Performance Metrics]
C --> G
D --> G
G --> H[Time Series DB]
end
subgraph "Alerting Layer"
F --> I[Alert Rules]
H --> I
I --> J[Notifications]
end
subgraph "Visualization"
F --> K[Log Explorer]
H --> L[Dashboards]
K --> M[Insights]
L --> M
end
style E fill:#00d9ff
style G fill:#ff006e
style I fill:#ffbe0b
Three Pillars of Observability
Logs : Detailed event records with contextMetrics : Quantitative measurements over timeTraces : Request flow through system components
📝 Structured Logging
1. Logging Architecture
JSON Structured Logging Format
import winston from 'winston' ;
import { v4 as uuidv4 } from 'uuid' ;
class AgentLogger {
constructor (options = {} ) {
this .agentId = options.agentId || process.env .AGENT_ID || uuidv4 ();
this .sessionId = options.sessionId || uuidv4 ();
this .environment = process.env .NODE_ENV || 'development' ;
this .logger = winston.createLogger ({
level : options.level || process.env .LOG_LEVEL || 'info' ,
format : winston.format .combine (
winston.format .timestamp ({ format : 'ISO' }),
winston.format .errors ({ stack : true }),
winston.format .json ()
),
defaultMeta : {
agent_id : this .agentId ,
session_id : this .sessionId ,
environment : this .environment ,
service : 'agentic-workflow' ,
version : process.env .APP_VERSION || '1.0.0'
},
transports : [
new winston.transports .Console ({
format : winston.format .combine (
winston.format .colorize (),
winston.format .printf (this .formatConsoleOutput .bind (this ))
)
}),
new winston.transports .File ({
filename : 'logs/agent-errors.log' ,
level : 'error' ,
maxsize : 10485760 ,
maxFiles : 5 ,
tailable : true
}),
new winston.transports .File ({
filename : 'logs/agent-combined.log' ,
maxsize : 10485760 ,
maxFiles : 10 ,
tailable : true
})
],
exitOnError : false
});
}
formatConsoleOutput (info ) {
const { timestamp, level, message, ...meta } = info;
const metaStr = Object .keys (meta).length ? JSON .stringify (meta, null , 2 ) : '' ;
return `${timestamp} [${level} ] ${message} ${metaStr ? '\n' + metaStr : '' } ` ;
}
logAgentStart (taskType, context = {} ) {
this .logger .info ('Agent execution started' , {
event_type : 'agent_start' ,
task_type : taskType,
...context
});
}
logAgentComplete (taskType, result, metrics = {} ) {
this .logger .info ('Agent execution completed' , {
event_type : 'agent_complete' ,
task_type : taskType,
status : result.status ,
duration_ms : metrics.duration ,
tokens_used : metrics.tokens ,
...result
});
}
logAgentError (taskType, error, context = {} ) {
this .logger .error ('Agent execution failed' , {
event_type : 'agent_error' ,
task_type : taskType,
error_message : error.message ,
error_stack : error.stack ,
error_code : error.code ,
...context
});
}
logMCPCall (toolName, params, context = {} ) {
this .logger .debug ('MCP tool call' , {
event_type : 'mcp_call' ,
tool_name : toolName,
params : this .sanitizeParams (params),
...context
});
}
logMCPResponse (toolName, response, duration ) {
this .logger .debug ('MCP tool response' , {
event_type : 'mcp_response' ,
tool_name : toolName,
duration_ms : duration,
response_size : JSON .stringify (response).length ,
status : 'success'
});
}
logMCPError (toolName, error, duration ) {
this .logger .error ('MCP tool error' , {
event_type : 'mcp_error' ,
tool_name : toolName,
duration_ms : duration,
error_message : error.message ,
error_code : error.code
});
}
logPerformance (operation, metrics ) {
this .logger .info ('Performance metrics' , {
event_type : 'performance' ,
operation,
duration_ms : metrics.duration ,
memory_mb : metrics.memory ,
cpu_percent : metrics.cpu
});
}
logSecurityEvent (eventType, details ) {
this .logger .warn ('Security event' , {
event_type : 'security' ,
security_event : eventType,
...details
});
}
sanitizeParams (params ) {
const sensitive = ['token' , 'password' , 'secret' , 'key' , 'api_key' ];
const sanitized = { ...params };
for (const key of Object .keys (sanitized)) {
if (sensitive.some (s =>toLowerCase ().includes (s))) {
sanitized[key] = '***REDACTED***' ;
}
}
return sanitized;
}
child (metadata ) {
const childLogger = Object .create (this );
childLogger.logger = this .logger .child (metadata);
return childLogger;
}
}
export default AgentLogger ;
const logger = new AgentLogger ({
agentId : 'pr-analyzer' ,
sessionId : process.env .GITHUB_RUN_ID
});
logger.logAgentStart ('pr-analysis' , {
pr_number : 123 ,
repository : 'owner/repo'
});
Python Structured Logging
import logging
import json
import sys
import os
from datetime import datetime
from typing import Any , Dict , Optional
import uuid
class JSONFormatter (logging.Formatter):
"""
JSON formatter for structured logging
"""
def __init__ (self ):
super ().__init__()
self .agent_id = os.getenv('AGENT_ID' , str (uuid.uuid4()))
self .session_id = os.getenv('GITHUB_RUN_ID' , str (uuid.uuid4()))
self .environment = os.getenv('ENVIRONMENT' , 'development' )
def format (self, record: logging.LogRecord ) -> str :
"""Format log record as JSON"""
log_data = {
'timestamp' : datetime.utcnow().isoformat() + 'Z' ,
'level' : record.levelname,
'message' : record.getMessage(),
'agent_id' : self .agent_id,
'session_id' : self .session_id,
'environment' : self .environment,
'service' : 'agentic-workflow' ,
'logger' : record.name,
'module' : record.module,
'function' : record.funcName,
'line' : record.lineno
}
if record.exc_info:
log_data['exception' ] = {
'type' : record.exc_info[0 ].__name__,
'message' : str (record.exc_info[1 ]),
'traceback' : self .formatException(record.exc_info)
}
if hasattr (record, 'extra_fields' ):
log_data.update(record.extra_fields)
return json.dumps(log_data)
class AgentLogger :
"""
Structured logger for agentic workflows
"""
def __init__ (self, name: str , level: str = 'INFO' ):
self .logger = logging.getLogger(name)
self .logger.setLevel(getattr (logging, level.upper()))
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(JSONFormatter())
self .logger.addHandler(console_handler)
if not os.path.exists('logs' ):
os.makedirs('logs' )
error_handler = logging.FileHandler('logs/agent-errors.log' )
error_handler.setLevel(logging.ERROR)
error_handler.setFormatter(JSONFormatter())
self .logger.addHandler(error_handler)
def log_agent_start (self, task_type: str , context: Dict [str , Any ] = None ):
"""Log agent start event"""
extra = {
'extra_fields' : {
'event_type' : 'agent_start' ,
'task_type' : task_type,
**(context or {})
}
}
self .logger.info('Agent execution started' , extra=extra)
def log_agent_complete (self, task_type: str , result: Dict [str , Any ], metrics: Dict [str , Any ] = None ):
"""Log agent completion event"""
extra = {
'extra_fields' : {
'event_type' : 'agent_complete' ,
'task_type' : task_type,
'status' : result.get('status' ),
'duration_ms' : metrics.get('duration' ) if metrics else None ,
**result
}
}
self .logger.info('Agent execution completed' , extra=extra)
def log_agent_error (self, task_type: str , error: Exception, context: Dict [str , Any ] = None ):
"""Log agent error event"""
extra = {
'extra_fields' : {
'event_type' : 'agent_error' ,
'task_type' : task_type,
'error_type' : type (error).__name__,
'error_message' : str (error),
**(context or {})
}
}
self .logger.error('Agent execution failed' , extra=extra, exc_info=error)
def log_mcp_call (self, tool_name: str , params: Dict [str , Any ], context: Dict [str , Any ] = None ):
"""Log MCP tool call"""
extra = {
'extra_fields' : {
'event_type' : 'mcp_call' ,
'tool_name' : tool_name,
'params' : self ._sanitize_params(params),
**(context or {})
}
}
self .logger.debug('MCP tool call' , extra=extra)
def _sanitize_params (self, params: Dict [str , Any ] ) -> Dict [str , Any ]:
"""Sanitize sensitive data"""
sensitive = ['token' , 'password' , 'secret' , 'key' , 'api_key' ]
sanitized = params.copy()
for key in sanitized.keys():
if any (s in key.lower() for s in sensitive):
sanitized[key] = '***REDACTED***'
return sanitized
logger = AgentLogger('pr-analyzer' , level='INFO' )
logger.log_agent_start('pr-analysis' , {'pr_number' : 123 })
2. Log Levels and When to Use Them
Log Level Guidelines
logger.error ('Failed to connect to MCP server' , {
error : error.message ,
mcp_server : 'github' ,
retry_count : 3
});
logger.warn ('MCP response slow' , {
duration_ms : 5000 ,
threshold_ms : 3000 ,
tool_name : 'github-search'
});
logger.info ('PR analysis completed' , {
pr_number : 123 ,
issues_found : 5 ,
duration_ms : 15000
});
logger.debug ('Processing file' , {
file_path : 'src/index.js' ,
file_size : 1024 ,
line_count : 50
});
logger.trace ('Token consumed' , {
token_count : 1000 ,
model : 'claude-3-5-sonnet' ,
prompt_type : 'analysis'
});
3. Log Aggregation Patterns
GitHub Actions Log Groups
steps:
- name: Run Agent Analysis
run: |
echo "::group::Agent Initialization"
node scripts/agents/pr-analyzer.js --phase=init
echo "::endgroup::"
echo "::group::MCP Server Connection"
node scripts/agents/pr-analyzer.js --phase=connect
echo "::endgroup::"
echo "::group::Analysis Execution"
node scripts/agents/pr-analyzer.js --phase=analyze
echo "::endgroup::"
echo "::group::Report Generation"
node scripts/agents/pr-analyzer.js --phase=report
echo "::endgroup::"
Centralized Log Collection
import { S3Client, PutObjectCommand } from '@aws-sdk/client-s3' ;
import { gunzipSync, gzipSync } from 'zlib' ;
class LogCollector {
constructor (options = {} ) {
this .s3Client = new S3Client ({
region : options.region || 'us-east-1'
});
this .bucket = options.bucket || 'agentic-workflow-logs' ;
this .compression = options.compression !== false ;
}
async uploadLogs (logFile, metadata = {} ) {
const content = await fs.readFile (logFile, 'utf8' );
const logs = content.split ('\n' ).filter (Boolean ).map (JSON .parse );
const key = this .generateLogKey (metadata);
const body = this .compression ? gzipSync (JSON .stringify (logs)) : JSON .stringify (logs);
await this .s3Client .send (new PutObjectCommand ({
Bucket : this .bucket ,
Key : key,
Body : body,
ContentType : 'application/json' ,
ContentEncoding : this .compression ? 'gzip' : undefined ,
Metadata : {
agent_id : metadata.agentId ,
session_id : metadata.sessionId ,
timestamp : new Date ().toISOString ()
}
}));
console .log (`✅ Logs uploaded to s3://${this .bucket} /${key} ` );
}
generateLogKey (metadata ) {
const date = new Date ();
const year = date.getUTCFullYear ();
const month = String (date.getUTCMonth () + 1 ).padStart (2 , '0' );
const day = String (date.getUTCDate ()).padStart (2 , '0' );
return [
'logs' ,
`year=${year} ` ,
`month=${month} ` ,
`day=${day} ` ,
`agent=${metadata.agentId} ` ,
`${metadata.sessionId} .json${this .compression ? '.gz' : '' } `
].join ('/' );
}
async queryLogs (filters = {} ) {
const params = {
Bucket : this .bucket ,
Key : filters.key ,
ExpressionType : 'SQL' ,
Expression : this .buildSQLQuery (filters),
InputSerialization : {
JSON : { Type : 'LINES' },
CompressionType : this .compression ? 'GZIP' : 'NONE'
},
OutputSerialization : {
JSON : { RecordDelimiter : '\n' }
}
};
}
}
export default LogCollector ;
📈 Metrics Collection
1. Performance Metrics
Agent Execution Metrics
import { performance } from 'perf_hooks' ;
import os from 'os' ;
class MetricsCollector {
constructor (this .metrics = new Map ();
this .startTime = performance.now ();
}
recordDuration (operation, duration ) {
this .recordMetric ('duration_ms' , operation, duration, 'histogram' );
}
recordCounter (name, value = 1 , labels = {} ) {
this .recordMetric (name, JSON .stringify (labels), value, 'counter' );
}
recordGauge (name, value, labels = {} ) {
this .recordMetric (name, JSON .stringify (labels), value, 'gauge' );
}
recordMetric (name, key, value, type ) {
const metricKey = `${name} :${key} ` ;
if (!this .metrics .has (metricKey)) {
this .metrics .set (metricKey, {
name,
type,
values : [],
labels : key !== 'default' ? JSON .parse (key) : {}
});
}
this .metrics .get (metricKey).values .push ({
value,
timestamp : Date .now ()
});
}
getSystemMetrics (const used = process.memoryUsage ();
const cpus = os.cpus ();
return {
memory : {
heap_used_mb : Math .round (used.heapUsed / 1024 / 1024 ),
heap_total_mb : Math .round (used.heapTotal / 1024 / 1024 ),
rss_mb : Math .round (used.rss / 1024 / 1024 ),
external_mb : Math .round (used.external / 1024 / 1024 )
},
cpu : {
count : cpus.length ,
model : cpus[0 ].model ,
load_avg : os.loadavg ()
},
uptime_seconds : Math .round (process.uptime ())
};
}
getAgentMetrics (const duration = performance.now () - this .startTime ;
return {
total_duration_ms : Math .round (duration),
operations_count : this .getOperationCount (),
errors_count : this .getErrorCount (),
success_rate : this .calculateSuccessRate ()
};
}
exportPrometheus (const lines = [];
for (const [key, metric] of this .metrics .entries ()) {
const { name, type, values, labels } = metric;
lines.push (`# HELP ${name} Agent workflow metric` );
lines.push (`# TYPE ${name} ${type} ` );
const labelStr = Object .entries (labels)
.map (([k, v] ) => `${k} ="${v} "` )
.join (',' );
if (type === 'histogram' ) {
const sorted = values.map (v =>value ).sort ((a, b ) => a - b);
lines.push (`${name} _sum{${labelStr} } ${sorted.reduce((a, b) => a + b, 0 )} ` );
lines.push (`${name} _count{${labelStr} } ${sorted.length} ` );
lines.push (`${name} _bucket{${labelStr} ,le="100"} ${sorted.filter(v => v <= 100 ).length} ` );
lines.push (`${name} _bucket{${labelStr} ,le="500"} ${sorted.filter(v => v <= 500 ).length} ` );
lines.push (`${name} _bucket{${labelStr} ,le="1000"} ${sorted.filter(v => v <= 1000 ).length} ` );
lines.push (`${name} _bucket{${labelStr} ,le="+Inf"} ${sorted.length} ` );
} else if (type === 'counter' ) {
const sum = values.reduce ((acc, v ) => acc + v.value , 0 );
lines.push (`${name} {${labelStr} } ${sum} ` );
} else if (type === 'gauge' ) {
const latest = values[values.length - 1 ];
lines.push (`${name} {${labelStr} } ${latest.value} ` );
}
}
return lines.join ('\n' );
}
exportJSON (const metrics = {
timestamp : new Date ().toISOString (),
agent : this .getAgentMetrics (),
system : this .getSystemMetrics (),
custom : {}
};
for (const [key, metric] of this .metrics .entries ()) {
metrics.custom [metric.name ] = {
type : metric.type ,
labels : metric.labels ,
values : metric.values
};
}
return metrics;
}
}
export default MetricsCollector ;
const metrics = new MetricsCollector ();
const start = performance.now ();
await performOperation ();
metrics.recordDuration ('pr_analysis' , performance.now () - start);
metrics.recordCounter ('files_analyzed' , 1 , { language : 'javascript' });
metrics.recordGauge ('active_mcp_connections' , 3 );
console .log (metrics.exportPrometheus ());
2. Custom Metrics for Agents
class AgentMetrics extends MetricsCollector {
recordLLMCall (model, tokens, duration ) {
this .recordCounter ('llm_calls_total' , 1 , { model });
this .recordCounter ('llm_tokens_total' , tokens, { model });
this .recordDuration ('llm_duration' , duration);
}
recordMCPTool (toolName, duration, success ) {
this .recordCounter ('mcp_calls_total' , 1 , { tool : toolName, success : success.toString () });
this .recordDuration ('mcp_duration' , duration);
}
recordDecision (decisionType, confidence ) {
this .recordCounter ('agent_decisions_total' , 1 , { type : decisionType });
this .recordGauge ('agent_confidence' , confidence, { type : decisionType });
}
recordCodeChanges (filesModified, linesAdded, linesRemoved ) {
this .recordCounter ('files_modified_total' , filesModified);
this .recordCounter ('lines_added_total' , linesAdded);
this .recordCounter ('lines_removed_total' , linesRemoved);
}
calculateCost (model, tokens ) {
const costs = {
'claude-3-5-sonnet' : 0.003 ,
'gpt-4-turbo' : 0.01 ,
'gpt-3.5-turbo' : 0.0015
};
const costPerToken = costs[model] || 0 ;
const cost = (tokens / 1000 ) * costPerToken;
this .recordGauge ('agent_cost_usd' , cost, { model });
return cost;
}
}
3. Metrics Visualization
GitHub Actions Job Summary
async function generateMetricsSummary (metrics ) {
const agentMetrics = metrics.getAgentMetrics ();
const systemMetrics = metrics.getSystemMetrics ();
await core.summary
.addHeading ('📊 Agent Execution Metrics' )
.addTable ([
[{data : 'Metric' , header : true }, {data : 'Value' , header : true }],
['Total Duration' , `${agentMetrics.total_duration_ms} ms` ],
['Operations' , agentMetrics.operations_count .toString ()],
['Errors' , agentMetrics.errors_count .toString ()],
['Success Rate' , `${agentMetrics.success_rate} %` ]
])
.addHeading ('💻 System Metrics' , 3 )
.addTable ([
[{data : 'Resource' , header : true }, {data : 'Usage' , header : true }],
['Memory (Heap)' , `${systemMetrics.memory.heap_used_mb} MB / ${systemMetrics.memory.heap_total_mb} MB` ],
['Memory (RSS)' , `${systemMetrics.memory.rss_mb} MB` ],
['CPU Count' , systemMetrics.cpu .count .toString ()],
['Uptime' , `${systemMetrics.uptime_seconds} s` ]
])
.write ();
}
🚨 Alerting Strategies
1. Alert Rules Configuration
name: Agent Monitoring & Alerts
on:
schedule:
- cron: '*/15 * * * *'
workflow_dispatch:
permissions:
contents: read
issues: write
jobs:
check-metrics:
name: Check Agent Metrics
runs-on: ubuntu-latest
steps:
- name: Fetch Recent Workflow Runs
id: fetch-runs
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const { data: runs } = await github.rest.actions.listWorkflowRunsForRepo({
owner: context.repo.owner,
repo: context.repo.repo,
status: 'completed',
per_page: 50
});
// Filter agent workflows
const agentRuns = runs.workflow_runs.filter(run =>
run.name.includes('Agent') || run.name.includes('Agentic')
);
return agentRuns;
- name: Calculate Metrics
id: metrics
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const runs = ${{ steps.fetch-runs.outputs.result }};
const total = runs.length;
const failed = runs.filter(r => r.conclusion === 'failure' ).length;
const successRate = total > 0 ? ((total - failed) / total) * 100 : 100 ;
const durations = runs.map(r => {
const start = new Date(r.created_at);
const end = new Date(r.updated_at);
return (end - start) / 1000 ;
});
const avgDuration = durations.reduce((a, b) => a + b, 0 ) / durations.length;
const p95Duration = durations.sort((a, b) => a - b)[Math.floor(durations.length * 0.95 )];
return {
total ,
failed ,
successRate ,
avgDuration ,
p95Duration
};
- name: Check Alert Conditions
id: check-alerts
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const metrics = ${{ steps.metrics.outputs.result }};
const alerts = [];
// Alert if success rate below 90 %
if (metrics.successRate < 90 ) {
alerts.push( {
severity: 'high' ,
title: 'Low Agent Success Rate' ,
message: `Success rate: $ {metrics.successRate.toFixed(1) }% (threshold: 90 %)` ,
metric: 'success_rate' ,
value: metrics.successRate
});
}
// Alert if p95 duration above 10 minutes
if (metrics.p95Duration > 600 ) {
alerts.push( {
severity: 'medium' ,
title: 'High Agent Latency' ,
message: `P95 duration: $ {Math.round(metrics.p95Duration) }s (threshold: 600s)` ,
metric: 'p95_duration' ,
value: metrics.p95Duration
});
}
// Alert if too many failures
if (metrics.failed > 5 ) {
alerts.push( {
severity: 'high' ,
title: 'Multiple Agent Failures' ,
message: `$ {metrics.failed } failures in last 50 runs` ,
metric: 'failure_count' ,
value: metrics.failed
});
}
return alerts;
- name: Create Alert Issues
if: steps.check-alerts.outputs.result != '[]'
uses: actions/github-script@v7
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
script: |
const alerts = ${{ steps.check-alerts.outputs.result }};
for (const alert of alerts) {
// Check if alert already exists
const { data: existingIssues } = await github.rest.issues.listForRepo( {
owner: context.repo.owner ,
repo: context.repo.repo ,
state: 'open' ,
labels: 'alert,monitoring' ,
per_page: 100
});
const exists = existingIssues.some(issue =>
issue.title === `🚨 $ {alert.title }`
);
if (!exists) {
await github.rest.issues.create( {
owner: context.repo.owner ,
repo: context.repo.repo ,
title: `🚨 $ {alert.title }` ,
body: `## Alert Details
**Severity**: $ {alert.severity.toUpperCase() }
**Metric**: $ {alert.metric }
**Value**: $ {alert.value }
$ {alert.message }
---
*Auto-generated alert from agent monitoring*
*Timestamp*: $ {new Date().toISOString() }` ,
labels: ['alert' , 'monitoring' , `severity:$ {alert.severity }` ]
});
}
}
- name: Send Slack Notification
if: steps.check-alerts.outputs.result != '[]'
env:
SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL }}
run: |
alerts='${{ steps.check-alerts.outputs.result }}'
curl -X POST "$SLACK_WEBHOOK_URL" \
-H 'Content-Type: application/json' \
-d "{
\"text\": \"🚨 Agent Monitoring Alert\",
\"blocks\": [
{
\"type\": \"header\",
\"text\": {
\"type\": \"plain_text\",
\"text\": \"🚨 Agent Monitoring Alert\"
}
},
{
\"type\": \"section\",
\"text\": {
\"type\": \"mrkdwn\",
\"text\": \"$alerts\"
}
}
]
}"
2. Real-Time Alerting
import { WebClient } from '@slack/web-api' ;
import nodemailer from 'nodemailer' ;
class AlertManager {
constructor (options = {} ) {
this .slackClient = options.slackWebhook ? new WebClient (options.slackToken ) : null ;
this .slackChannel = options.slackChannel ;
this .emailTransporter = options.emailConfig ? nodemailer.createTransporter (options.emailConfig ) : null ;
this .thresholds = options.thresholds || this .getDefaultThresholds ();
}
getDefaultThresholds (return {
duration : {
warning : 300000 ,
critical : 600000
},
errorRate : {
warning : 0.05 ,
critical : 0.10
},
memory : {
warning : 512 ,
critical : 1024
}
};
}
checkThreshold (metric, value ) {
const threshold = this .thresholds [metric];
if (!threshold) return null ;
if (value >= threshold.critical ) {
return { level : 'critical' , threshold : threshold.critical };
} else if (value >= threshold.warning ) {
return { level : 'warning' , threshold : threshold.warning };
}
return null ;
}
async sendAlert (alert ) {
const promises = [];
if (this .slackClient ) {
promises.push (this .sendSlackAlert (alert));
}
if (this .emailTransporter ) {
promises.push (this .sendEmailAlert (alert));
}
await Promise .allSettled (promises);
}
async sendSlackAlert (alert ) {
const emoji = alert.level === 'critical' ? '🔴' : '⚠️' ;
const color = alert.level === 'critical' ? 'danger' : 'warning' ;
await this .slackClient .chat .postMessage ({
channel : this .slackChannel ,
text : `${emoji} ${alert.title} ` ,
blocks : [
{
type : 'header' ,
text : {
type : 'plain_text' ,
text : `${emoji} ${alert.title} `
}
},
{
type : 'section' ,
fields : [
{
type : 'mrkdwn' ,
text : `*Severity:*\n${alert.level.toUpperCase()} `
},
{
type : 'mrkdwn' ,
text : `*Metric:*\n${alert.metric} `
},
{
type : 'mrkdwn' ,
text : `*Value:*\n${alert.value} `
},
{
type : 'mrkdwn' ,
text : `*Threshold:*\n${alert.threshold} `
}
]
},
{
type : 'section' ,
text : {
type : 'mrkdwn' ,
text : alert.message
}
},
{
type : 'context' ,
elements : [
{
type : 'mrkdwn' ,
text : `Agent: ${alert.agentId} | Session: ${alert.sessionId} `
}
]
}
],
attachments : [
{
color : color,
text : `View logs: ${alert.logsUrl} `
}
]
});
}
async sendEmailAlert (alert ) {
await this .emailTransporter .sendMail ({
from : 'alerts@example.com' ,
to : 'team@example.com' ,
subject : `[${alert.level.toUpperCase()} ] ${alert.title} ` ,
html : `
<h2>${alert.level === 'critical' ? '🔴' : '⚠️' } ${alert.title} </h2>
<table>
<tr><th>Severity</th><td>${alert.level.toUpperCase()} </td></tr>
<tr><th>Metric</th><td>${alert.metric} </td></tr>
<tr><th>Value</th><td>${alert.value} </td></tr>
<tr><th>Threshold</th><td>${alert.threshold} </td></tr>
</table>
<p>${alert.message} </p>
<p><a href="${alert.logsUrl} ">View Logs</a></p>
`
});
}
}
export default AlertManager ;
🐛 Debugging Techniques
1. Debug Mode
process.env .DEBUG = 'agent:*,mcp:*' ;
process.env .LOG_LEVEL = 'debug' ;
import createDebug from 'debug' ;
const debug = createDebug ('agent:pr-analyzer' );
const debugMCP = createDebug ('mcp:github' );
debug ('Starting PR analysis for #%d' , prNumber);
debugMCP ('Calling tool %s with params %o' , toolName, params);
2. Interactive Debugging
steps:
- name: Setup tmate session
if: failure()
uses: mxschmitt/action-tmate@v3
with:
limit-access-to-actor: true
timeout-minutes: 30
3. Trace Analysis
import { trace, context, SpanStatusCode } from '@opentelemetry/api' ;
class AgentTracer {
constructor (serviceName = 'agentic-workflow' ) {
this .tracer = trace.getTracer (serviceName);
}
async traceOperation (name, fn, attributes = {} ) {
const span = this .tracer .startSpan (name, {
attributes : {
'agent.service' : 'agentic-workflow' ,
...attributes
}
});
try {
const result = await context.with (
trace.setSpan (context.active (), span),
fn
);
span.setStatus ({ code : SpanStatusCode .OK });
return result;
} catch (error) {
span.setStatus ({
code : SpanStatusCode .ERROR ,
message : error.message
});
span.recordException (error);
throw error;
} finally {
span.end ();
}
}
async traceMCPCall (toolName, params, fn ) {
return this .traceOperation (
`mcp.${toolName} ` ,
fn,
{
'mcp.tool' : toolName,
'mcp.params' : JSON .stringify (params)
}
);
}
}
const tracer = new AgentTracer ();
await tracer.traceOperation ('pr-analysis' , async () => {
await tracer.traceMCPCall ('github-get-pr' , { prNumber }, async () => {
});
});
📊 Observability Best Practices
1. Correlation IDs
import { v4 as uuidv4 } from 'uuid' ;
class CorrelationContext {
constructor (this .correlationId = uuidv4 ();
this .parentId = null ;
}
createChild (const child = new CorrelationContext ();
child.parentId = this .correlationId ;
return child;
}
getHeaders (return {
'X-Correlation-ID' : this .correlationId ,
'X-Parent-ID' : this .parentId
};
}
}
const ctx = new CorrelationContext ();
logger.info ('Starting operation' , {
correlation_id : ctx.correlationId ,
parent_id : ctx.parentId
});
2. Health Checks
export async function checkHealth (const health = {
status : 'healthy' ,
timestamp : new Date ().toISOString (),
checks : {}
};
try {
await fetch ('http://localhost:3000/health' );
health.checks .mcp_server = { status : 'up' };
} catch (error) {
health.checks .mcp_server = { status : 'down' , error : error.message };
health.status = 'unhealthy' ;
}
const used = process.memoryUsage ();
const memoryPercent = (used.heapUsed / used.heapTotal ) * 100 ;
health.checks .memory = {
status : memoryPercent < 90 ? 'healthy' : 'warning' ,
heap_used_mb : Math .round (used.heapUsed / 1024 / 1024 ),
heap_total_mb : Math .round (used.heapTotal / 1024 / 1024 ),
usage_percent : Math .round (memoryPercent)
};
return health;
}
3. Dashboards
export function generateDashboard (metrics, logs ) {
return `
<!DOCTYPE html>
<html>
<head>
<title>Agent Metrics Dashboard</title>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
</head>
<body>
<h1>📊 Agentic Workflow Metrics</h1>
<div class="metrics">
<div class="metric-card">
<h3>Success Rate</h3>
<div class="value">${metrics.successRate} %</div>
</div>
<div class="metric-card">
<h3>Avg Duration</h3>
<div class="value">${metrics.avgDuration} ms</div>
</div>
<div class="metric-card">
<h3>Error Count</h3>
<div class="value">${metrics.errorCount} </div>
</div>
</div>
<canvas id="durationChart"></canvas>
<script>
const ctx = document.getElementById('durationChart');
new Chart(ctx, {
type: 'line',
data: {
labels: ${JSON .stringify(metrics.timestamps)} ,
datasets: [{
label: 'Duration (ms)',
data: ${JSON .stringify(metrics.durations)} ,
borderColor: 'rgb(75, 192, 192)',
tension: 0.1
}]
}
});
</script>
</body>
</html>
` ;
}
📚 Related Skills
🔗 References
Logging & Monitoring
GitHub Actions
Observability Tools
✅ Remember Checklist When implementing logging and monitoring for agentic workflows:
License : Apache-2.0Version : 2.0.0Last Updated : 2026-04-02Maintained by : Hack23 Organization
🔗 Integration with Riksdagsmonitor agentic workflows This gh-aw skill is applied by the 11 agentic news workflows in .github/workflows/news-*.md. Their domain contract (analysis-artifact product, gate, article contract) lives in:
