One agent is easy to trust, and a dozen coordinating agents in production are a system you have to watch and steer. Relevant Software gives you that operating layer: observability across every agent, orchestration that coordinates them, and guardrails your team sets.
Every agent action becomes visible, from the prompt it received to the tool it called and the output it returned.
Your agents work as one system: routing, hand-offs, and shared state coordinated under rules your team sets. Our agentic engineering services build and deploy those agents, and this engagement coordinates and watches them once they run.
Each release meets a test suite before it reaches users, and keeps meeting it on live traffic.
Every agent acts inside boundaries your team defines: what it may do on its own, what needs a human, and what it never touches. Where those boundaries protect sensitive data and access, our AI security services team hardens what each agent is allowed to reach.
Token spend, latency, and throughput stay in view per agent and per workflow, so a growing fleet stays affordable. Our MLOps team runs the pipelines and infrastructure the monitoring reports on.
When an agent behaves outside its limits, your team pauses, flags, or rolls it back in minutes.
Your live agents, their tools, and their data flows go through a short discovery, and you leave with a map of what to watch and control first.
Tracing, logging, and metrics wrap every agent and tool call, so the first dashboards light up within the initial weeks.
Routing, hand-offs, and shared state reach a design your engineers sign off before it touches production traffic.
Validation, permissions, and evaluation suites run in your pipeline, gating each release against the standard you set.
Cost budgets, alerts, and one-step rollback move into production, so scale and safety hold together.
Every dashboard, runbook, and control transfers to your team in a package they operate without us in the room.
ISO 27001, GDPR, and HIPAA-aligned delivery. The monitoring and audit trail we set up meet the standards your reviewers already accept.
96% employee retention. The engineers who instrument your agents stay on to tune the orchestration and evals as the fleet grows.
Relevant AI Delivery Framework™: up to 50% faster delivery and up to 20% fewer bugs through AI-assisted development, with every change still passing senior review.
200+ companies have built and scaled products with Relevant, and the case study below is the checkable subset.
Open Banking, HL7 FHIR, and PSD2 delivery experience, so audit-trail and compliance questions get answered from projects already shipped.
Your models, your cloud, your tools. The observability and orchestration layer plugs into the frameworks and platforms your agents already use, so you keep your stack.
Scope sets the price, and you can estimate yours from a few variables: how many agents and workflows you run, how much traffic they handle, how many environments need coverage, and whether the engagement includes orchestration and incident tooling or monitoring on its own. Our team reviews each variable with you on the first call and itemizes it in the proposal.
Run a quick estimate today: count your live agents and the external tools each one can call, because that product, more than agent count alone, drives the instrumentation effort. If that inventory is hard to pull together, our team maps it with you as the first step, with nothing to prepare beforehand.
You see the first traces and dashboards within the first two weeks, and orchestration, evals, and incident controls follow over the weeks after, scaled to how many agents and environments the program spans. Access sets the pace: the sooner your engineers share how the agents are deployed, the sooner instrumentation lands.
One check you can run this week: ask whether your team can name every external tool and API your agents call, and where they can, instrumentation moves fast. Where that list is scattered, our team traces it with you at the start.
AI agent monitoring and orchestration operates the agents you already run: it instruments them, coordinates them, and keeps them inside guardrails. Building and deploying those agents is separate work that an agentic engineering engagement covers.
Most teams reach us once the agents exist and the question shifts from whether they can build it to what it is doing in production. A short way to tell which you need: if the agents are live and you lack visibility, this engagement fits, and if you still need the agents built, that work starts earlier.
Yes, we can monitor and orchestrate agents another team or vendor built. The engagement starts by mapping the live system: the agents, the tools and APIs each one can reach, and the data that moves through them. From there instrumentation wraps the agents as they run, with no rebuild required, and orchestration sits around them rather than inside a rewrite.
A rebuild enters the conversation only where an agent exposes no seam to instrument, and that case is rare. If you are unsure how your current agents are wired, our team traces it with you before any change.
Both models work, and the scope decides. For a monitoring setup on a handful of agents, one or two senior engineers cover it alongside your team for a few weeks. For orchestration across a large fleet, or coverage that runs beside continuous agent development, a dedicated team carries the load.
A signal for choosing: where a single reliability or audit deadline drives the work, a scoped engagement fits, and where new agents ship every sprint, standing coverage pays off faster.
We work with the stack your team already runs, and instrument against open standards so you avoid lock-in [tool examples to confirm with your engineers: OpenTelemetry, LangSmith, Langfuse, Arize]. Where you have no tooling yet, we recommend a setup that fits your scale and budget rather than the largest platform on the market.
If you want that mapped to your agents before any commitment, contact us and our team scopes it with you.
We stand up the monitoring, orchestration, and guardrails, and hand your team the operating layer.
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