AI agent deployment and DevOps.
Meld Origins helps teams deploy and operate AI agents and workflows on their existing cloud. We build CI/CD pipelines, evaluation gates, secrets management, tracing, alerts and rollback procedures with a documented handover.
Discuss your systemWHAT WE BUILD
Deployment and operations for AI agents.
Deployment is only the beginning. AI systems also need identity, versioning, evaluation, monitoring, and a way to recover from failure.
Safe delivery
CI/CD, environment configuration, tests, staged releases, and practical rollback plans.
Reliable operation
Queues, scaling, rate limits, timeouts, secrets, and resilient integrations around the agent.
Actionable visibility
Traces, metrics, cost and latency budgets, evaluation signals, and incident runbooks.
OPERATING MODEL
What we put in place.
The exact tools depend on your cloud and stack. The operational responsibilities stay the same.
Identity and secrets
Give each service and tool the minimum access it needs. Keep credentials out of prompts, logs, and source control; rotate them through your existing secret-management process.
Release gates
Run software tests and representative agent evaluations before promotion. Record which prompt, model, tool schema, and workflow version produced a result.
Observability
Connect user requests to model calls, tool actions, queues, and database work. Alert on failures that affect users, not just infrastructure health.
Incident response
Define retries, circuit breakers, human escalation, rollback, and ownership so an unhealthy agent can be paused without taking down the product.
USE CASES
Where DevOps becomes the bottleneck.
These problems usually appear as soon as an agent moves from a pilot to a shared production workflow.
Agent deployments
Ship agents with isolated environments, approved configuration, deployment history, and controlled access to tools and data.
Workflow operations
Run long-lived multi-step jobs with durable state, idempotency, queue visibility, and exception handling.
Model and tool changes
Compare outcomes before switching models or prompts, monitor cost and latency, and keep a fast rollback path.
Data access services
Protect MCP servers and other data tools with authentication, policy checks, query limits, and auditability. See our enterprise Text-to-SQL MCP note.
QUESTIONS
Questions before production.
A short conversation can identify the biggest operational risks in the current system.
Is this MLOps or DevOps?
It includes both familiar software operations and AI-specific work such as prompt and model versioning, evaluation gates, tool-call traces, and cost monitoring.
Can you use our cloud and CI/CD tools?
Yes. We start with your existing cloud, deployment pipeline, security policies, and team ownership model, then fill the important gaps.
How do you avoid logging sensitive prompts or results?
Telemetry should capture enough context to diagnose failures while applying redaction, access control, and retention policies to sensitive content.
What does the handover include?
Infrastructure and pipeline documentation, alerts and dashboards, runbooks, rollback instructions, and a walkthrough with the team that will operate it.
Can you take over a prototype that is already built?
We can review its deployment, tool permissions, failure handling and evaluation coverage, then scope the gaps before production. If the underlying APIs or jobs need work, we can include backend engineering in that scope.
CONTACT
Tell us what needs to work.
A useful first conversation starts with the workflow, constraints, and what success would look like.
Let’s make it real.
Share a brief outline. Sourish will reply directly to discuss scope, feasibility, and a sensible first milestone.
support@vyanlabs.in ↗