It’s enough AI experiments in your enterprise. You need agents that run in production, integrate with existing systems, and operate within security and compliance boundaries that your team can enforce. That’s what Relevant’s agentic engineering services deliver.
We design and build AI agents tailored to your internal workflows, business systems, and operational requirements.
We automate repetitive and semi-complex operational processes across your organization with autonomous agents.
Relevant deploys AI agents in isolated, containerized environments built to your enterprise security, access control, and infrastructure standards.
We build the operational layer that keeps deployed agents under control, monitored, and auditable throughout their lifecycle.
We embed AI agents and automation into your software delivery lifecycle to improve engineering velocity and reduce repetitive operational work.
Experience shows that enterprise AI agent programs fail because agents behave unpredictably in production or because deployments can’t pass a security review. We at Relevant build for both from the start.
You get a clear map of where agents create value across your operations, engineering, and security teams. A structured discovery by Relevant surfaces the workflows, repetitive tasks, and process bottlenecks worth automating.
You receive an architecture blueprint and governance framework before any agent development begins. It covers model choices, integration patterns, security controls, and observability requirements, with SOC 2, GDPR, HIPAA, and industry-specific compliance constraints.
You get working agents, multi-agent workflows, and process automation built against the approved architecture, with senior engineering judgment behind every integration and decision boundary.
Your agents run in isolated, containerized environments configured to your enterprise security, access control, and infrastructure standards, with support for self-hosted, hybrid, or air-gapped setups. Access permissions, secrets management, audit logging, and observability are all in place before any agent touches live data.
Your agents integrate with your existing systems and pass production-grade testing, including accuracy validation, security review, load testing, and human-in-the-loop verification across real workflows. The rollout reaches you in phases: pilot users first, then expanded scope, then full production, so you control the pace and confirm results at each stage.
The agents run in production with full monitoring, governance, and audit trail in place, and your team takes operational ownership with documentation, runbooks, and training. You can also keep Relevant engineers on as an ongoing AI engineering retainer, providing month-over-month tuning, scaling, and new-agent development from the people who built the system.
Our team has delivered 246 projects across industries and stacks, including AI agents and automation programs operating in enterprise production environments. The patterns we apply to your agent architecture are the ones we have already deployed, governed, and scaled.
We deploy AI agents in isolated containerized environments configured to your enterprise security and infrastructure standards. Self-hosted, hybrid, and air-gapped configurations are supported. Access controls and audit logging are built into every deployment from the start.
Every agent we deploy includes behavior monitoring, output validation, guardrails, and rollback capability. Agents that go into production are agents you can govern.
Agent behavior is determined by what we build, not by the model’s defaults. You work with 92% senior talent who design every prompt, decision boundary, and integration with the understanding that this code will run autonomously inside your production systems.
Agent programs require ongoing tuning, scaling, and new-agent development. That work goes faster when the engineers who designed the original architecture are still on the team. With 96% employee retention, the people who ship your first agents can keep improving the system as it grows.
Most enterprise processes that look like agent candidates are actually better served by simpler automation like scripts, integrations, or workflow tools, without a language model in the loop.
Agents add value when a process requires interpreting unstructured input, making judgment calls across multiple decision boundaries, or coordinating actions across systems that lack clean APIs. If the process has clear inputs, deterministic logic, and stable interfaces, traditional automation is the right answer, and we will recommend it over an agent.
The audit phase of the engagement evaluates each process under consideration against three criteria: what the agent would actually decide, what could go wrong if the decision is incorrect, and whether the value of the automation justifies the operational overhead of running and governing an agent. We scope out processes that fail any of those criteria and either build them from scratch or route them to simpler automation patterns.
Model selection is part of the architecture phase and is driven by each agent’s requirements.
At Relevant, we design the architecture to make models replaceable. Agent behavior, prompts, and integrations are decoupled from the specific model being called, so when a better model is released or pricing changes, you can migrate the agent with bounded engineering effort rather than rebuilding.
Agent behavior is shaped by four mechanisms layered together:
Every agent we deploy has its decision boundaries explicitly defined during architecture — what it can do autonomously, what requires human review, what is escalated and never automated. We can also configure those boundaries post-deployment, so as your team gains confidence with the agent’s behavior, the autonomous scope can expand within the governance controls already in place.
You define the measurement instrumentation during the design phase, and our team ships agents according to it. Standard metrics include the volume of work the agent processes, the rate at which its outputs are accepted versus reverted by humans in the loop, the time savings compared to the manual baseline, and the cost per transaction, including model inference and operational overhead.
There are also custom metrics specific to the agent’s domain, for example, accuracy rates, processing latency, and downstream business impact. We add them as needed, per the use case.
Operational ownership transitions in accordance with a model defined at the start of the engagement. There are three common configurations:
For teams taking a full handoff, the skills required are standard enterprise software operations: observability and monitoring, incident response, change management, and familiarity with the specific governance and behavior controls each agent uses.
We explicitly document each agent’s decision boundaries, guardrails, and tuning parameters, and provide training to your operations and security teams during the rollout phase.
AI-assisted development helps reduce agent build time by up to 50% and cut bugs by up to 20% compared to traditional enterprise approaches. The speed comes from senior engineers using the right tools instead of handing decisions over to AI.
Regulatory constraints (SOC 2, GDPR, HIPAA, and industry-specific frameworks) inform the agent architecture from the discovery phase onward. The deployment that ships to your enterprise is compliant by construction.
We have a 4.9 Clutch rating based on third-party validation from verified clients who highlight transparency, communication, and delivery quality.
Book a call with our experts to discuss your automation priorities, agent architecture, or deployment requirements. Get a clear program roadmap before you commit to the build.
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