# Webinaire Conduktor Agents en direct, en français Webinaire # Laissez les corvées aux agents Kafka Mercredi 25 novembre · 11h00 CET *L'expertise Kafka ne devrait pas reposer sur quelques personnes d'une seule équipe.*Conduktor Agents, ce sont des agents IA dans Console qui l'apportent à chaque équipe. Ils vérifient vos clusters, repèrent ce qui demande de l'attention et apportent le correctif à l'équipe responsable, pour que vos experts Kafka puissent se consacrer à de nouveaux projets. - 45 minutes - Démo en direct - Questions-réponses en direct **Aurelie Marcuzzo**Solutions Architect **Jean-Louis Boudart**Head of Customer Success Engineering ## Réservez votre place Vous ne pouvez pas y assister en direct ? Inscrivez-vous quand même, nous vous enverrons l'enregistrement. ## Qui devrait participer ? - Ingénieurs plateforme et administrateurs Kafka - SRE et ingénieurs d'astreinte - Développeurs d'applications - Équipes sécurité et conformité - Responsables techniques ## Pourquoi nous avons créé Conduktor Agents Vos experts Kafka passent une grande partie de leur semaine sur la maintenance : tickets, demandes d'accès, toujours les mêmes questions. Les agents prennent ce travail en charge, pour que vos experts se remettent à créer et que chaque équipe obtienne de l'aide quand elle en a besoin. > « La plupart de nos problèmes Kafka, nous les avions déjà vus : un pic de coûts que personne ne pouvait attribuer à une équipe, des accès que personne ne se souvenait avoir accordés, ou un connecteur arrêté pendant la nuit. Désormais, les agents les signalent avant qu'ils ne deviennent des incidents, et le correctif nous attend déjà. » VP Platform Engineering, banque du Fortune 100 ## Ce que les agents font pour chaque équipe Home Ask Conduktor Agents Console Topics Schema registry Consumer groups Kafka Connect Kafka Gateway Brokers Service accounts SQL engines Analytics Insights Chargeback Lineage Settings main Local Kafka local-kafka-cluster Consumer Groups orders-service orders Ask A orders consume rate 412.6 msg/s offset lag 49,142 time lag 14m 12s Search by name 1-6 of 6 Partition Member Consume rate End offset Commit Offset lag 0 **orders-service-1***/10.4.2.17**3F2A91C4* 71.2 *msg/s* 2,418,906 2,418,715 191 1 **orders-service-1***/10.4.2.17**3F2A91C4* 69.8 *msg/s* 2,402,331 2,402,109 222 2 **orders-service-2***/10.4.2.18**B7D0E655* 70.4 *msg/s* 2,395,772 2,395,428 344 3 **orders-service-2***/10.4.2.18**B7D0E655* 0.0 *msg/s* 2,431,118 2,382,908 48,210 4 **orders-service-3***/10.4.2.19**9C41AD08* 68.9 *msg/s* 2,409,540 2,409,381 159 5 **orders-service-3***/10.4.2.19**9C41AD08* 72.1 *msg/s* 2,421,087 2,420,871 216 orders-service Stable Topics*1* Members*3* Alerts*0* ****Graphs Topic Partitions Members Offset lag Time lag **orders** 6 3 49,142 14m 12s Search by any field Name Status Active Last trigger Destination No alerts New Alert Alert details Alert destination Name*** Alert name Alert ownership*** Define who can manage this alert. This cannot be changed after creation. **Group **Application **Only me Kafka cluster local Consumer group orders-service Metric*** OffsetLag Select a metric Target topic*** All topics Operator > Value*** 10000 0 Comments Write your notes... External Send alert notifications to external tools for team-wide visibility **** Slack Teams Webhook Email Destination channel Enter or select a channel Check if your alert is working Send test Cancel Next Save Why orders-service is falling behind How can I help? Ask about your topics, consumer groups, schemas or how Conduktor works. Is this cluster healthy? Which consumer groups are lagging? Which topics have no consumer? How do I give a team its own topics? Why is orders-service falling behind? Thinking… Reading your cluster… Looked at your cluster · 2 calls · 6s Read consumer group orders-service*64 ms* Read topic orders*212 ms* Almost all of the lag is on partition 3 of orders: 48,210 of 49,142 messages. That partition is assigned to orders-service-2. Its commits stopped 14 minutes ago, while the other two instances keep up with the topic. Worth checking that instance first: the consumer is still in the group, so it is not a rebalance. Alert me if lag on orders goes over 10,000. Thinking… Working on the page… Worked on the page · 10 calls · 9s Went to orders-service*402 ms* Clicked Alerts 0*288 ms* Clicked Create an alert*251 ms* Filled Name with orders-service-lag-over-10k*296 ms* Picked Only me in Alert ownership*241 ms* Picked OffsetLag in Metric*337 ms* Picked > in Operator*312 ms* Filled Value with 10000*274 ms* Clicked Next*263 ms* Left Save for you to press*18 ms* The alert is filled in but not saved yet. Press **Save** to create it. Send it to Slack Also watch time lag Ask anything… /for skills AI can make mistakes, check what matters. Ask Conduktor Saves the alert. You'll hear about it when lag on orders passes 10,000. Dismiss Home Ask Conduktor Agents Console Topics Schema registry Consumer groups Kafka Connect Kafka Gateway Brokers Service accounts SQL engines Analytics Insights Chargeback Lineage Settings main Ask A Local Connect Refresh Add connector Search by name... Class Type State 1-8 of 8 Connector name Type State Tasks Failed Tasks ********** ********** ********** ********** ********** ********** ********** ********** datagen-customers DatagenConnector Running 1 0 datagen-customers-json DatagenConnector Running 1 0 datagen-pageviews DatagenConnector Running 1 0 datagen-payroll-employee DatagenConnector Running 1 0 datagen-payroll-employee-location DatagenConnector Running 1 0 postgres-sink JdbcSinkConnector Running 0 1 postgres-sink-dlq JdbcSinkConnector Running 1 0 wikipedia ServerSentEventsSourceConnector Running 1 0 Unblock postgres-sink: send pageviews offset 6282 to a DLQ and move past it Approval Failing sink connector, find the poison pill Pending Approved Produced 3 hours ago · agent Failing sink connector Reject Approve Output Trace Pending your decision: nothing has been applied The agent wrote no changes. Approving records your decision for the steps below; a person carries them out. Approved by admin@demo.dev a few seconds ago Finding **Connector:** postgres-sink reads pageviews. Its task 0 is FAILED (0 of 1 tasks running). Proposed steps 1. Stop postgres-sink on Connect cluster local-connect. 2. Create the dead-letter topic pageviews.dlq with 1 partition. 3. Copy pageviews partition 0 offset 6282 to pageviews.dlq, then read it back. 4. Reset connect-postgres-sink on pageviews partition 0 from 6282 to 6283. 5. Resume postgres-sink, restart task 0 and check the lag keeps falling. 6. Tell the team producing to pageviews about the record. 7. Add errors.tolerance=all and a dead-letter queue to postgres-sink. Why postgres-sink is failing **How can I help?**Ask about your topics, consumer groups, schemas or how Conduktor works. Which connectors are failing? Why is a connector task failing? Why is postgres-sink failing? Thinking… Reading your cluster… Looked at your cluster · 4 calls · 14s Listed connectors on local-connect*41 ms* Read connector postgres-sink*84 ms* Read connector task postgres-sink*112 ms* Searched topic records on local*230 ms* The postgres-sink task is failing because it hit a record on pageviews that isn't Confluent-Avro encoded. The connector still shows RUNNING, but its only task is FAILED (0 of 1 running). The record that broke it is pageviews, partition 0, offset 6282: a single plain-JSON message in a topic of Avro records. Open the approval Show offset 6282 Ask anything… /for skills AI can make mistakes, check what matters. Approve this proposal? Approving **Unblock postgres-sink: send pageviews offset 6282 to a DLQ and move past it** records your decision. Nothing is executed. The steps the agent proposed are for someone to carry out. Reason (optional) Kept with the approval, for whoever reads it later Cancel Approve Home Ask Conduktor Agents Console Topics Schema registry Consumer groups Kafka Connect Kafka Gateway Brokers Service accounts SQL engines Analytics Insights Chargeback Lineage Settings main Local Kafka local-kafka-cluster Service Accounts legacy-etl Ask A Service Accounts New ACL Search by name... 1-8 of 8 Name app-a app-b app-c app-x bi-tool legacy-etl migration-job reporting-svc legacy-etl View in Lineage Add label ACLs All resource ACLs assigned to this service account. **Topics** *(1)* * Host: * All Alter AlterConfigs Create Delete DescribeConfigs Describe Read Write + Add Topic ACL **Consumer Groups** *(1)* * Host: * All Describe Delete Read + Add Consumer Group ACL Over-privileged service accounts How can I help? Ask about your topics, consumer groups, schemas or how Conduktor works. Is this cluster healthy? Which consumer groups are lagging? Which topics have no consumer? How do I give a team its own topics? Who has more access than they need? Thinking… Reading your cluster… Looked at your cluster · 2 calls · 12s Listed service accounts*52 ms* Listed acl bindings*140 ms* Two accounts stand out on local-kafka-cluster. - legacy-etl holds All on every topic and every consumer group, from any host. It should be scoped to the topics and groups the ETL job uses. - bi-tool has Read on the prefix p, which matches payments-* and payroll-*. That is probably wider than intended. Open legacy-etl Who uses bi-tool? Ask anything… /for skills AI can make mistakes, check what matters. Home Ask Conduktor Agents Console Topics Schema registry Consumer groups Kafka Connect Kafka Gateway Brokers Service accounts SQL engines Analytics Insights Chargeback Lineage Settings main Ask A Lineage view Live** Topics** Graph Data flow Back Select a Service account to start Search for a resource Service accounts**8** Connections**142** Topics**49** Show Topics Show Service accounts User:legacy-etl 49 49 User:bi-tool 0 8 User:app-c 5 5 User:app-x 4 4 User:migration-job 1 0 User:app-a 3 3 User:app-b 4 4 pageviews payment-transactions payments-incoming payroll-employee payroll-employee-location shipment-tracking all service accounts > payroll-employee **Live 49 49 User:legacy-etl 0 8 User:bi-tool 4 4 User:app-x payroll-employee customers pageviews aws.identity.cdc.customers.json.v1 insurance-customers payroll-employee-location payments-incoming 1.4 KB/s ************************ Personal data and who can read it **How can I help?**Ask about your topics, consumer groups, schemas or how Conduktor works. *Is this cluster healthy?**Which consumer groups are lagging?**Which topics have no consumer?**How do I give a team its own topics?* Which topics hold personal data, and who can read them? Thinking… Reading your cluster… Looked at your cluster · 9 calls · 21s Listed topics*45 ms* Checked topic insights*53 ms* Searched topic records*230 ms* Read topic payroll-employee*88 ms* Read topic customer-profiles*91 ms* Listed acl bindings*140 ms* Read service account legacy-etl*62 ms* Read service account bi-tool*58 ms* Read lineage payroll-employee*310 ms* Six topics hold personal data in clear. The most sensitive is payroll-employee, with SSNs and pay rates. legacy-etl can read it through All on every topic, and bi-tool through Read on the prefix p. Neither account is owned by an application. Open payroll-employee Who should own legacy-etl? Ask anything… /for skills AI can make mistakes, check what matters. Home Ask Conduktor Agents Console Topics Schema registry Consumer groups Kafka Connect Kafka Gateway Brokers Service accounts SQL engines Analytics Insights Chargeback Lineage Settings main Ask A Chargeback *View by***Application** *Show***All costs** 7D 30D 6M 12M YTD **Export as CSV** Overall cost $87,611.00 $44,266.00 **20.11%***since 30D ago* **29.81%***since 30D ago* Storage cost $28,206.00 $13,323.00 **3.35%***since 30D ago* **3.67%***since 30D ago* Partition cost $22,611.00 $10,450.00 **3.23%***since 30D ago* **2.59%***since 30D ago* Ingress $9,561.00 $4,671.00 **13.27%***since 30D ago* **21.62%***since 30D ago* Egress $27,233.00 $15,822.00 **56.97%***since 30D ago* **77.83%***since 30D ago* **Key Metrics** **Storage **Partitions **Ingress **Egress Cost Percentage ********** **$4K****$2K** **$3K****$1.5K** **$2K****$1K** **$1K****$500** **$0****$0** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** ******** 26 Sep 30 Sep 04 Oct 08 Oct 12 Oct 16 Oct 20 Oct 24 Oct Search applications 1-4 of 4 1-1 of 1 Application 26 Sep 27 Sep payments **$1.4K***49.82% 100%* **$1.4K***49.68% 100%* insurance **$606***21.69%* **$612***21.79%* ecommerce **$520***18.61%* **$523***18.63%* analytics **$276***9.88%* **$278***9.9%* Payments cost this week **How can I help?**Ask about your topics, consumer groups, schemas or how Conduktor works. Is this cluster healthy? Which consumer groups are lagging? Which topics have no consumer? How do I give a team its own topics? Why did payments cost more this week? Thinking… Reading your cluster… Looked at your cluster · 1 call · 2s Read chargeback payments*1.2 s* Egress is the main cause, up about 1,488 (3,362 → 4,850) of the 1,802 increase. Bytes out rose from roughly 50–70 GB a day to 70–90 GB a day. Show payments by day Which topics drove egress? Ask anything… /for skills AI can make mistakes, check what matters. ### Vous gardez le contrôle - **D'abord des rapports**Chaque agent commence par des rapports. Vous lui en confiez davantage quand vous lui faites confiance. - **Votre approbation**Les changements proposés par un agent attendent votre approbation par défaut. - **Sa propre identité**Chaque agent agit pour une personne nommée, avec uniquement les permissions de cette personne. - **Limites et traces**Chaque exécution a des limites strictes et une trace complète. Un agent qui s'emballe s'arrête de lui-même. [Réserver ma place](#register) Mercredi 25 novembre · 11h00 CET · 45 minutes ## Avec Console, l'IA répond juste sur Kafka Nous avons posé à la même IA les mêmes questions sur un grand cluster Kafka, sans Console puis avec Console. Sans Console **13%**réponses correctes L'IA ne voit que Kafka brut. Elle devine. Avec Console **90%**réponses correctes L'IA voit ce que Console sait : qui utilise quoi, à qui ça appartient, ce que ça coûte. Benchmarks des agents Conduktor, septembre 2026. **Combien de temps dure le webinaire ?** 45 minutes : une courte introduction, une démo en direct, puis vos questions. **Et si je ne peux pas y assister en direct ?** Inscrivez-vous quand même. Nous vous enverrons l'enregistrement par e-mail après la session. **Puis-je poser des questions ?** Oui. Posez-les à tout moment dans le chat, Aurelie et Jean-Louis y répondent à la fin. **Quelles données sont envoyées au modèle d'IA ?** Les agents travaillent surtout à partir de métadonnées, comme les noms de topics, les configurations, le lag et les permissions, et ne lisent les messages que pour les tâches qui en ont besoin. Ces données sont envoyées au modèle que vous avez connecté, sur votre propre compte. Avec un modèle que vous hébergez vous-même, rien ne quitte votre réseau. Rien ne passe par Conduktor. **Faut-il déjà utiliser Conduktor ?** Non. La démo montre Conduktor Console avec les agents, et s'adresse à toutes les équipes qui exploitent Kafka ou développent des applications avec Kafka. Les agents sont inclus dans Console, et vous pourrez les essayer ensuite. **Avec quels Kafka et quels modèles cela fonctionne-t-il ?** Avec tout cluster auquel Console se connecte, que vous l'exploitiez vous-même ou via un service managé comme Amazon MSK, Confluent Cloud ou Aiven. Les agents utilisent le modèle de votre choix, sur votre propre compte : Claude via Anthropic, des modèles sur Amazon Bedrock, OpenAI ou tout endpoint compatible OpenAI. **En quelle langue se déroule le webinaire ?** En français. Vous préférez l'anglais ? [Voir la session en anglais](https://www.conduktor.io/events/webinars/conduktor-agents-live?src=webinar-fr). ## Découvrez Conduktor Agents en direct le 25 novembre Mercredi 25 novembre, 11h00 CET. Une démo en direct et vos questions, avec Aurelie Marcuzzo et Jean-Louis Boudart. [Réserver ma place](#register)