Automations
This pillar covers software delivery workflows where agents plan tasks, write tests, run CI pipelines, diagnose failures, and coordinate pull request or deployment actions. Content should show how custom SDLC automation improves engineering throughput, reduces repetitive toil, and connects coding agents to repositories, environments, and release governance.
This foundational page outlines the architecture for a custom, multi-agent SDLC system where specialized agents coordinate planning, coding, testing, and deployment tasks. It details how orchestration across repositories, CI/CD pipelines, and observability tools reduces engineering toil, accelerates release cycles, and provides measurable throughput gains for software teams building AI into their delivery process.
This page details a custom workflow where AI agents analyze pull requests, suggest improvements, and autonomously resolve merge conflicts by understanding semantic context. It explains how this architecture reduces reviewer burnout, accelerates merge velocity, and integrates with GitHub, GitLab, and internal code quality gates to maintain standards without manual bottleneck.
This page covers a custom automation workflow that parses product requirements and user stories to generate comprehensive test suites, including edge cases and integration scenarios. It shows how this reduces manual test planning effort, improves coverage, and connects Jira/Productboard with test management and CI systems for continuous validation.
This page explains a proactive agentic workflow that monitors dependency graphs, assesses CVEs, generates safe upgrade PRs, and runs regression tests before merging. It details the architecture for reducing security debt and maintenance overhead, integrating SCA tools, CI pipelines, and approval gates for high-risk changes.
This page describes a custom orchestration workflow for managing synchronized changes across microservices or monorepos, where agents analyze dependencies, coordinate branching, and validate cross-service contracts. It demonstrates how this prevents integration failures, reduces coordination meetings, and uses graph-based analysis for large-scale codebases.
This page outlines an automation workflow where agents parse code commits, detect architectural shifts, and auto-generate or update system diagrams in tools like Lucidchart or Draw.io. It shows how this eliminates outdated documentation, improves onboarding, and maintains a living architectural view integrated with the VCS.
This page details a workflow where agents validate OpenAPI/Swagger specs against live implementations, flag drift, and auto-update documentation portals. It explains the architecture for ensuring API reliability, reducing support tickets, and connecting spec repositories, testing frameworks, and developer portals.
This page covers a custom RAG-powered workflow that allows developers to query codebases using natural language, retrieving relevant functions, decisions, and tribal knowledge. It demonstrates how this reduces time spent navigating legacy systems, integrates with IDEs and Slack, and improves developer onboarding and efficiency.
This page explains an agentic workflow that ingests regulatory policies (e.g., SOC2, HIPAA), translates them into automated security and code checks, and enforces them in pull requests and deployments. It details the architecture for audit-ready compliance, reducing manual control reviews, and integrating policy engines with GitOps pipelines.
This page describes a custom automation workflow where agents monitor pipeline health, diagnose failures (e.g., flaky tests, resource exhaustion), and execute remediation scripts. It shows how this reduces mean-time-to-repair (MTTR) for builds, optimizes cloud spend, and integrates observability with Jenkins, GitLab, or GitHub Actions.
This page outlines a workflow where agents spin up ephemeral, production-like test environments on-demand based on pull requests and automatically tear them down after validation. It details the architecture for reducing environment contention and cloud costs, using Kubernetes and IaC tools like Terraform.
This page covers a multi-agent workflow that deploys canaries, analyzes real-user metrics and error rates, and autonomously decides to promote or rollback based on SLOs. It explains how this reduces deployment risk, accelerates safe releases, and integrates with observability platforms like Datadog and launchdarkly.
This page details a custom workflow where agents analyze build patterns, predict cache invalidation, and manage distributed cache layers to minimize build times. It demonstrates the architecture for cutting CI costs and developer wait times, integrating with tools like Bazel, Gradle, or custom artifact repositories.
This page explains a workflow where agents run automated performance benchmarks on feature branches, compare results against baselines, and block merges that introduce regressions. It shows how this prevents performance debt, integrates load testing tools, and provides actionable insights to developers pre-merge.
This page describes an agentic workflow that forecasts CI workload, dynamically provisions and scales cloud runners (e.g., AWS EC2, GCP VMs), and shuts down idle resources. It details the architecture for reducing cloud spend by 30-50%, integrating with cloud APIs and pipeline schedulers.
This page outlines a workflow where agents ingest SAST/DAST findings, correlate them with code context and historical data, and route only validated, high-priority issues to developers. It demonstrates how this reduces alert fatigue, improves security team efficiency, and connects Snyk, Checkmarx, or Wiz with Jira.
This page covers a workflow where, post-incident, agents automatically gather deployment logs, change sets, and metrics to generate a preliminary root-cause analysis and blameless report. It explains how this accelerates post-mortems, improves learning, and integrates PagerDuty, Git, and observability data.
This page details an intelligent workflow where agents analyze code changes to predict impacted tests, dynamically split test suites, and parallelize execution across CI workers. It shows the architecture for slashing pipeline duration, optimizing test resource usage, and integrating with test frameworks and VCS.
This page explains a governance workflow where agents manage artifact repositories, enforce promotion policies from dev to prod, and automatically clean up stale artifacts. It details how this ensures traceability, reduces storage costs, and integrates with JFrog Artifactory, Nexus, and deployment gates.
This page describes a workflow where AI agents monitor E2E test failures, differentiate between flaky tests and real bugs, and automatically refactor or regenerate brittle selectors. It demonstrates how this reduces QA maintenance overhead by 70%, using computer vision and DOM analysis integrated with Selenium/Cypress.
This page outlines a custom automation workflow where agents capture UI screenshots, compare them against baselines using computer vision, and flag visual drifts for review. It details the architecture for catching unintended UI changes, integrating with Percy or Applitools, and routing issues to designers or developers.
This page covers a workflow where agents simulate complex user journeys by interpreting natural language scripts, interacting with the UI, and validating business outcomes. It explains how this replaces manual scenario testing, improves coverage, and integrates with analytics and CRM systems for realistic data.
This page details a workflow where agents run automated accessibility audits against WCAG standards on every commit, generate remediation tickets, and track compliance over time. It shows the architecture for proactive accessibility, reducing legal risk, and integrating axe-core with CI and issue trackers.
This page explains a workflow where agents analyze test execution history, network, and timing data to identify flaky tests, automatically quarantine them, and notify owners. It demonstrates how this stabilizes CI/CD pipelines, improves developer trust, and uses statistical analysis integrated with test runners.
This page describes an AI-Ops workflow where agents analyze metrics, logs, and traces to predict incidents before SLO breaches, correlate alerts, and route them with context to the right on-call engineer. It details the architecture for reducing MTTR and alert noise, integrating with Datadog, Splunk, and PagerDuty.
This page outlines a workflow where agents ingest streaming logs, detect anomalous patterns, correlate them across services, and suggest root causes using topology maps. It shows how this accelerates debugging, reduces mean-time-to-identification (MTTI), and integrates with Elasticsearch and OpenTelemetry.
This page covers a workflow where, upon confirmed incidents, agents retrieve and execute predefined runbooks (e.g., restart service, scale capacity), escalating only when human judgment is required. It explains the architecture for standardizing response, reducing manual toil, and integrating with ServiceNow and chatOps.
This page details a workflow where agents continuously calculate SLOs/SLIs from telemetry, forecast error budget consumption, and trigger automated governance actions (e.g., freeze releases). It demonstrates how this operationalizes SRE practices, integrates with Google's SLO toolkit or Nobl9, and provides actionable dashboards.
This page explains a workflow where, after incident resolution, agents synthesize timeline data, contributor actions, and resolution steps into a draft post-mortem document. It shows how this reduces administrative overhead, ensures consistency, and integrates with Confluence or Notion for team review and approval.
This page details an industry-specific workflow where agents monitor regulatory updates (e.g., Basel III), map them to code and configuration requirements, and generate implementation tickets and test plans. It demonstrates the architecture for reducing compliance risk and implementation lag in FinTech SDLC.
This page covers a workflow for payment processors where agents continuously scan code, infrastructure, and processes against PCI-DSS controls, generating evidence and remediation tickets. It explains how this automates audit preparation, reduces manual control testing, and integrates with security and deployment tools.
This page outlines a healthcare-specific workflow where agents enforce PHI data handling rules in CI/CD, mask test data, and generate audit trails for deployments. It details the architecture for achieving and proving HIPAA compliance in devops, integrating with EHR systems and IAM.
This page describes a workflow for life sciences where agents automate the validation of CTMS software changes against 21 CFR Part 11, executing test scripts and documenting results. It shows how this accelerates validation cycles, reduces manual QA, and ensures regulatory readiness for system updates.
This page details a retail workflow where agents manage the full lifecycle of A/B tests: generating variant code, deploying, analyzing real-time performance, and rolling out winners. It demonstrates the architecture for increasing experimentation velocity, integrating with Optimizely, and connecting to business metrics.
This page explains a telecom-specific workflow where agents orchestrate the testing, certification, and deployment of containerized network functions across distributed edge clusters. It details how this meets carrier-grade reliability demands, integrates with OSS/BSS, and automates compliance with 3GPP standards.
This page covers an automotive workflow where agents simulate vehicle ECUs, test OTA update packages for safety and integration, and manage phased rollouts with rollback triggers. It shows the architecture for reducing recall risk, complying with ISO 26262, and integrating with automotive CI/CD pipelines.
This page outlines a platform engineering workflow where agents power a self-service portal, fulfilling developer requests for environments, databases, and permissions via automated, policy-governed workflows. It details how this reduces platform team toil, standardizes provisioning, and uses Backstage or similar.
This page describes a workflow where agents continuously compare live cloud infrastructure to IaC definitions, detect drift, and either auto-remediate or generate pull requests for review. It demonstrates the architecture for enforcing infrastructure consistency, using Terraform Cloud and cloud provider APIs.
This page details a workflow where agents monitor Kubernetes cluster metrics, application demand, and cost signals to autonomously adjust node pools, requests/limits, and HPA parameters. It shows how this optimizes performance and cost, integrating with K8s operators and cloud billing APIs.
This page explains a workflow where agents analyze proposed schema changes, generate safe migration and rollback scripts, execute them in staging, and coordinate production cutovers with zero-downtime. It details the architecture for reducing DBA manual work and deployment risk, using Liquibase or Flyway.
This page covers a DevSecOps workflow where agents integrate threat modeling into sprint planning, analyzing user stories and architecture diagrams to generate security stories and test cases. It demonstrates how this shifts security left, using tools like OWASP Threat Dragon and Jira integration.
This page outlines a workflow where agents automatically generate SBOMs for every build, analyze them for license compliance and vulnerability chaining, and enforce policies in CI. It details the architecture for meeting regulatory requirements (e.g., CISA), using Syft and Grype integrated into pipelines.
This page describes a custom MLOps workflow where agents automate model training, validation, versioning, and deployment, including canary releases and performance monitoring. It shows the architecture for accelerating AI product iteration, integrating MLflow, Kubeflow, and model registries.
This page details a DataOps workflow where agents monitor data pipelines for freshness and quality, detect statistical drift in features, and trigger retraining or alerts. It explains how this ensures reliable analytics and ML, integrating with Airflow, Great Expectations, and observability platforms.
This page covers a specialized workflow for managing LLM lifecycles, where agents orchestrate dataset preparation, fine-tuning jobs, evaluation against benchmarks, and safe deployment to inference endpoints. It details the architecture for operationalizing generative AI, using tools like Weights & Biases and vLLM.
This page explains a business integration workflow where agents parse high-priority tickets, create feature branches, and after development and testing, automatically deploy the fix to production with appropriate approvals. It demonstrates how this closes the loop between ops and dev, reducing resolution time for critical issues.
How We Work
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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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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