Automations
This pillar focuses on telecom and network operations workflows that predict usage spikes, reroute traffic, and trigger remediation actions when latency, congestion, or failures appear. Content should show how self-healing network automation improves availability, reduces operator toil, and supports enterprise-grade connectivity reliability.
This foundational page details a custom, multi-agent orchestration workflow that predicts congestion, reroutes traffic, and triggers remediation actions to maintain service levels autonomously. It explains the architecture for integrating telemetry, predictive models, and network controllers to reduce operator toil and improve availability. The implementation context covers enterprise and carrier-grade networks, focusing on the ROI from reduced MTTR and operational overhead.
This page outlines a custom workflow that uses time-series forecasting and agentic logic to predict traffic spikes and proactively adjust routing policies before congestion impacts users. It covers the integration of streaming telemetry with SDN controllers and the business impact of preventing SLA violations and revenue loss from degraded performance. Implementation details include anomaly detection models, policy engines, and rollback safeguards.
This page describes a custom, agent-driven architecture where specialized AI agents monitor latency and packet loss, negotiate optimal paths, and execute BGP or SD-WAN changes in real-time. It focuses on the orchestration logic needed for coordinated action across network domains and the commercial upside in improved application performance and user experience. The build covers agent communication, decision hierarchies, and integration with platforms like Cisco ACI or VMware SD-WAN.
This page details a custom automation workflow that detects physical or logical link failures, diagnoses root cause via correlated telemetry, and automatically provisions alternative paths or triggers repair tickets. It explains the architecture for combining fault detection, topology-aware rerouting, and integration with NOC systems to slash MTTR. The implementation context includes optical, MPLS, and data center interconnect environments.
This page covers a custom workflow where AI agents identify anomalous latency increases, trace them to specific network segments or external peers, and implement traffic engineering changes like cost adjustments or local offload. It highlights the business value in preserving real-time application performance for trading, VoIP, and gaming. The architecture involves continuous baseline comparison, path analysis agents, and API-driven control of routers and switches.
This page explains a custom, logic-heavy workflow that continuously evaluates BGP path attributes, incorporates real-time performance and cost data, and makes safe, incremental adjustments to outbound routing policies. It targets the reduction of transit costs and improvement of inter-AS performance for service providers and large enterprises. Implementation covers BGP monitoring stacks, decision agents, and controlled session manipulation via automation platforms.
This page details a custom orchestration layer where agents interpret application performance metrics, business intent, and cost constraints to dynamically adjust SD-WAN steering policies. It shows how to build a system that moves beyond static policies to responsive, application-aware routing, improving WAN economics and user satisfaction. The build integrates with vendors like Cisco, VMware, and Fortinet via their APIs and includes approval gates for major policy shifts.
This page outlines a custom workflow where AI agents analyze server health, application demand patterns, and security events to autonomously adjust load balancer pool weights, persistence settings, and health checks. It delivers uptime and efficiency gains for web-scale and enterprise applications. The architecture details integration with F5 BIG-IP, NGINX, or cloud load balancers, using observability data to drive configuration-as-code updates.
This page describes a custom workflow for telecom operators to dynamically create, modify, and assure 5G network slices based on real-time demand from enterprise customers or IoT applications. It covers the multi-domain orchestration across RAN, transport, and core, tying the build to new revenue streams and service agility. Implementation involves intent translation, resource reservation agents, and integration with OSS/BSS and 5G core functions.
This page details a custom security and traffic engineering workflow that detects DDoS patterns, automatically redirects malicious traffic to scrubbing centers, and updates edge ACLs or BGP blackhole routes. It focuses on reducing mitigation time from minutes to seconds, protecting revenue and infrastructure. The architecture combines flow analysis, threat intelligence, and coordination between on-prem gear and cloud scrubbers like Cloudflare or Akamai.
This page explains a custom workflow that forecasts bandwidth and device utilization trends, then triggers procurement workflows, cloud circuit upgrades, or virtual network function scaling before bottlenecks occur. It translates to capital efficiency and prevented outages. The build involves time-series forecasting models, inventory APIs, and integration with procurement systems (e.g., ServiceNow) and cloud consoles (AWS, Azure) for automated provisioning.
This page outlines a custom workflow where agents continuously audit network device configurations against security baselines (CIS, NIST) and business rules, then generate and apply corrective changes autonomously. It eliminates configuration drift and reduces security vulnerability windows. Implementation covers network CMDB integration, diff analysis, and safe deployment via tools like Ansible or Salt, with rollback and human approval for high-risk changes.
This page details a custom, end-to-end ZTP workflow where agents handle device discovery, image validation, policy assignment, and integration into monitoring—all without manual CLI intervention. It accelerates deployment cycles for branch offices and data center expansions. The architecture includes DHCP/PXE servers, image repositories, template engines, and post-provisioning validation agents integrated with ITSM tools.
This page describes a custom workflow that analyzes real-user metrics, content popularity, and origin server health to dynamically adjust CDN caching rules and traffic steering between edge POPs. It improves content delivery performance and reduces origin load for media and e-commerce companies. The build involves integrating RUM data, CDN provider APIs (Akamai, Fastly), and logic to optimize for cost vs. performance trade-offs.
This page outlines a custom workflow where AI agents correlate alarms, perform initial root cause analysis, and create pre-populated, prioritized tickets in ServiceNow or Jira with recommended actions. It drastically reduces NOC alert fatigue and speeds up Level 1 triage. Implementation covers integration with monitoring tools (SolarWinds, Dynatrace), NLP for alarm parsing, and routing logic based on service impact and team skills.
This page details a custom workflow that ingests streams of network alarms, uses graph-based models to identify the underlying fault, and suppresses redundant alerts to present operators with a single probable cause. It cuts through noise to accelerate mean time to innocence. The architecture explains dependency mapping, event correlation engines, and integration with AIOps platforms for continuous learning from past incidents.
This page describes a custom workflow where detection of a threat (e.g., IOC from SIEM) triggers autonomous agents to push updated ACLs, segment firewall rules, or isolate VLANs across the network infrastructure. It closes the security response loop from hours to seconds. The build focuses on orchestration between Palo Alto Networks, Cisco ISE, or Fortinet FortiGate APIs and requires robust change control and audit trails.
This page outlines a custom workflow where agents assess vulnerability scans, download approved patches/firmware, stage them in maintenance windows, execute upgrades, and validate device health post-update—all with rollback capabilities. It reduces security exposure and manual patching labor. Implementation covers integration with vulnerability managers, device APIs, and change management systems to ensure compliance and minimal service disruption.
This page details a custom workflow that monitors IP utilization trends, predicts subnet exhaustion, and automatically recommends or implements DHCP scope changes, subnet splits, or supernetting. It prevents address starvation incidents that can halt device onboarding. The architecture integrates with IPAM tools like Infoblox or BlueCat, using forecasting logic to drive API-based configuration updates.
This page explains a custom workflow that continuously measures performance against SLAs (latency, jitter, packet loss), generates violation alerts, and compiles compliance reports for customers or internal stakeholders. It automates a manual, error-prone reporting process, improving billing accuracy and customer trust. The build involves data aggregation from probes and monitors, calculation engines, and document generation integrated with CRM systems.
This page describes a custom workflow where agents draft network change requests based on required outcomes, route them through the appropriate technical and business approval chains, schedule execution in maintenance windows, and coordinate pre/post validation checks. It enforces compliance and reduces change-related outages. Implementation integrates with ServiceNow, Jira, and network automation platforms, embedding risk assessment and backout plans.
This page outlines a custom workflow focused on real-time media quality (MOS, jitter), where agents detect degradation, pinpoint the network segment responsible (LAN, WAN, ISP), and trigger QoS policy adjustments or rerouting before user calls drop. It protects business-critical unified communications. The architecture integrates with UC platforms like Cisco UCM or Microsoft Teams Direct Routing and SD-WAN controllers for remedial action.
This page details a custom, large-scale workflow for telecom operators to optimize traffic flows across their national or global backbone based on cost, latency, and peer congestion. It uses multi-agent systems to make frequent, granular adjustments to MPLS-TE or SRv6 policies, improving utilization and margins. The build involves high-frequency telemetry, path computation elements (PCE), and integration with optical and IP layers.
This page explains a custom workflow for service providers to automatically monitor the health of thousands of customer MPLS VPNs, detect issues like route leakage or PE-CE link failure, and execute corrective actions like backup path activation. It ensures SLA compliance for premium enterprise services. Implementation covers BGP/MPLS monitoring, customer topology awareness, and safe manipulation of VRFs and route targets.
This page describes a custom workflow for securely onboarding and segmenting massive IoT fleets, where agents classify device types, assign them to dynamic network segments (microsegments), and enforce traffic policies to limit lateral movement. It scales IoT deployments while containing security risks. The architecture integrates with NAC (Cisco ISE, Aruba ClearPass), IoT platforms, and SDN controllers for policy enforcement.
This page outlines a custom, ultra-sensitive workflow that continuously monitors microsecond-level latency on trading routes, and if thresholds are breached, automatically fails over to pre-provisioned low-latency backup paths or adjusts microwave/LFN links. It directly protects trading revenue. The build involves FPGA-accelerated monitoring, integration with specialized trading switches (Arista, Mellanox), and extremely deterministic orchestration logic.
This page details a custom workflow for media companies where agents monitor viewer buffer health and last-mile conditions, then dynamically instruct CDNs or origin servers to adjust video bitrates and steer traffic to less congested delivery paths. It optimizes viewer experience and reduces CDN egress costs. Implementation integrates with players, CDN APIs, and deep packet inspection to make real-time routing decisions.
This page describes a custom workflow for hospital networks where agents identify critical medical devices (ventilators, infusion pumps) via MAC/IP, automatically assign them to a high-priority QoS queue, and ensure their traffic is never dropped or delayed. It supports clinical safety and network reliability. The architecture involves integration with network access control and clinical asset management systems to dynamically apply policies.
This page outlines a custom workflow for industrial environments where agents detect anomalies in OT traffic (e.g., PLCs, SCADA), automatically isolate affected cells via firewall rules or VLAN changes, and trigger failover to redundant controllers to maintain production. It balances security containment with operational continuity. The build integrates with industrial firewalls (Claroty, Nozomi), OT monitors, and Purdue Model-aware segmentation policies.
This page details a custom workflow that monitors application performance and cost in a hybrid cloud environment, then automatically adjusts Direct Connect/AWS VPN or ExpressRoute/Azure VPN configurations, or shifts workloads between clouds, to optimize for performance and budget. It manages the complexity of multi-cloud networking. Implementation involves cloud health APIs, cost management tools, and SD-WAN or cloud router orchestration.
This page explains a custom workflow for gaming platforms and esports venues where agents monitor player-to-server latency, automatically reroute traffic through optimal internet exchanges or peered paths, and provision edge compute resources during tournaments to maintain sub-millisecond response times. It is critical for player fairness and viewer experience. The architecture integrates with game servers, ISP APIs, and global traffic managers.
This page focuses on the financial ROI of self-healing automation, detailing a custom workflow architecture that quantifies reduced truck rolls, lower NOC headcount needs, and avoided outage penalties. It connects technical capabilities—like automated diagnostics and remediation—directly to OPEX savings, providing a business case for investment. Implementation covers cost attribution, savings tracking, and integration with financial systems.
This page details a custom workflow designed specifically to eliminate single points of failure and automate failover across every layer (link, device, path, data center) to achieve and prove five-nines availability. It covers redundancy design, continuous validation of backup paths, and automated failover testing. The business impact is on service credibility and premium SLAs for critical infrastructure operators.
This page outlines a custom workflow that attacks MTTR by orchestrating a sequence of agents for rapid fault isolation, parts replacement dispatch, configuration restoration, and service validation. It turns a multi-hour manual process into a sub-15-minute automated procedure. The architecture integrates telemetry, CMDB, spare parts inventory, and field service management systems into a single orchestrated runbook.
This page describes a custom workflow focused on cost arbitrage, where agents continuously evaluate the price of different transit providers, direct peers, and cloud egress, and shift traffic to the lowest-cost path that meets performance SLAs. It directly reduces monthly network spend for data-intensive businesses. Implementation involves BGP monitoring, pricing feeds, and safe traffic shifting logic with performance guardrails.
This page details a custom workflow that translates high-level business intent (e.g., 'prioritize Salesforce') into low-level network configurations, continuously audits the network state for compliance with that intent, and automatically corrects drift. It moves networks from static configuration to dynamic assurance. The build covers intent translation engines, state validation agents, and integration with Cisco DNA Center or similar controllers.
This page explains a custom workflow where a live digital twin of the network is continuously updated with real configs and telemetry. Agents use this twin to simulate the impact of proposed changes or failure scenarios, approving only safe actions for production. It prevents costly misconfigurations and enables proactive planning. Implementation involves graph databases, simulation engines, and bi-directional sync with network devices.
This page describes a custom workflow that embeds AIOps principles, using multiple agents for anomaly detection, incident correlation, predictive failure forecasting, and automated remediation recommendation. It elevates network operations from reactive to predictive. The architecture details the integration of ML models for time-series analysis, graph analysis for root cause, and orchestration with existing ITIM tools.
This page outlines a custom workflow for directing user and IoT device traffic to the optimal edge compute node based on latency, node capacity, and data locality requirements. It is essential for latency-sensitive edge applications. The build involves global server load balancing, edge health monitoring, and integration with Kubernetes or edge platforms to make dynamic routing decisions.
This page details a custom workflow for managing traffic across hybrid terrestrial-satellite networks, where agents monitor satellite link quality (weather, congestion), and dynamically route traffic between GEO/LEO satellites and ground stations to maintain connectivity. It supports remote operations and global coverage. Implementation involves specialized satellite modems, path calculation agents, and integration with SD-WAN for seamless failover.
This page describes a custom workflow where agents analyze traffic loads and power usage of network devices, putting idle ports, switches, or entire chassis into low-power states during off-peak hours without impacting redundancy. It reduces energy costs and supports sustainability goals. The architecture integrates with power monitoring PDUs, network device APIs, and requires careful validation to avoid impacting availability.
This page details a custom workflow that deeply integrates network automation with ServiceNow or similar ITSM platforms. Agents read change requests, plan the technical implementation, execute it within the approved window, and update the CMDB and ticket status—all within the ITSM workflow. It bridges the gap between ITIL processes and network DevOps, improving auditability and coordination.
This page outlines a custom workflow where security incidents detected by the SOC (e.g., via Splunk, Sentinel) automatically trigger network containment actions, such as isolating a compromised endpoint via NAC or blocking malicious IPs at the firewall. It creates a unified security and network response. Implementation focuses on secure, API-driven integration between SIEM/SOAR platforms and network control points, with mandatory approval loops for critical assets.
This page describes a custom workflow where agents collect, normalize, and pipeline diverse network data (NetFlow, SNMP, syslog, device configs) from across the estate into a centralized data lake (Snowflake, Databricks) for advanced analytics and ML model training. It automates the tedious data engineering required for network analytics. The architecture covers scalable collectors, schema management, and quality validation agents.
This page details a custom workflow where network changes (like opening a security group or provisioning a VPC peering) are automatically coordinated with application deployments in AWS, Azure, or GCP. Agents interpret infrastructure-as-code templates and execute the necessary network-side configurations in lockstep. It eliminates manual coordination errors in cloud-native environments, speeding up deployment cycles.
This page outlines a custom workflow that classifies network alerts by severity and impacted service, then uses on-call schedules and skill matrices to route notifications via PagerDuty, Slack, or MS Teams to the right engineer with contextual data. It reduces alert noise and ensures faster expert engagement. Implementation involves integration with monitoring tools, on-call management APIs, and enrichment of alerts with runbook links.
This security-focused page details a custom workflow where upon detection of a compromised host (via EDR or NDR), agents automatically quarantine the entire subnet or VLAN by pushing firewall deny-all rules and updating NAC policies, containing the threat's lateral movement. It focuses on reducing blast radius. The build requires tight integration with EDR platforms and network enforcement points, with clear escalation paths for false positives.
This page describes a custom workflow that dynamically adjusts ZTNA policies based on user behavior, device posture, and threat intelligence. Agents can temporarily restrict access or require step-up authentication without manual admin intervention. It enforces least-privilege access adaptively. Implementation integrates with ZTNA providers (Zscaler, Netskope) or NGFWs, using contextual signals to drive policy API calls.
This page outlines a custom workflow where fresh threat intelligence feeds (IPs, domains) are automatically ingested, validated, and converted into BGP blackhole or null-route announcements across the network edge to drop malicious traffic before it enters. It operationalizes threat feeds at network speed. The architecture covers feed aggregation, risk scoring agents, and controlled BGP session manipulation with routers.
This page details a custom workflow for MSPs and service providers where agents automatically provision CPE devices (routers, firewalls) for new customers, push standardized configs, and then continuously monitor their health and performance against SLAs. It scales managed service delivery. Implementation involves zero-touch provisioning pipelines, configuration templates, and integration with customer-facing portals for visibility.
This page explains a custom workflow for cloud providers and MSPs to automatically create and manage isolated network slices (VRFs, VLANs, security policies) for each tenant on shared physical infrastructure, ensuring strict isolation and compliance. It enables secure multi-tenancy at scale. The build leverages network virtualization platforms and orchestrates across physical, virtual, and containerized network functions.
This page describes a custom workflow for large carriers where agents analyze traffic flows to peers and transit providers, continuously optimizing BGP communities and local preference to minimize cost and maximize performance for wholesale services. It automates a complex, manual traffic engineering process. Implementation involves high-scale flow analysis, commercial data integration, and safe BGP policy optimization agents.
This page outlines a custom workflow that simulates end-user experience from key locations, detects degradation before the customer reports it, and triggers remediation to avoid breaching SLAs and incurring financial penalties. It shifts from reactive SLA reporting to proactive assurance. The architecture integrates synthetic transaction monitoring, real-user monitoring, and network automation to fix issues preemptively.
This page details a custom workflow for modern data center fabrics (Cisco ACI, Arista) where agents monitor buffer utilization and microbursts on spine-leaf links, dynamically adjusting ECMP hashing or pausing certain traffic flows to alleviate congestion and prevent packet drops. It optimizes east-west traffic performance for applications. Implementation uses switch telemetry (sFlow, INT) and direct API integration with fabric controllers.
This page describes a custom workflow that, when a VM or container is deployed, analyzes network policies, security requirements, and current utilization to automatically place it in the correct VLAN, VXLAN, or security group and connect it to the necessary services. It enforces network policy at the moment of compute provisioning. The build integrates with vCenter, Kubernetes, and cloud orchestration platforms via their APIs.
This page outlines a custom workflow focused on the massive east-west flows within hyperscale environments, using agents to analyze application dependencies and dynamically adjust routing within the Clos fabric to minimize latency and maximize bisectional bandwidth. It is critical for distributed database and AI training workloads. Implementation involves custom agents integrated with the data center's own network operating system and orchestration layer.
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One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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