Swappable framework layers, from test scenarios to the final scorecard
Industries tested with real agent workflows, healthcare and finance
Rebuilds needed to add a new provider or workflow, done through config
Cortega.ai
Maryland, USA
AI Governance / Agentic Security
Amazon Bedrock, Amazon EKS, Amazon Aurora (PostgreSQL), Amazon VPC, OpenTelemetry
Cortega.ai builds an AI governance platform that authenticates, inspects, blocks, redacts, and routes AI traffic, including LLM calls, agent traffic, and MCP tool calls, before it leaves a customer’s infrastructure boundary. Ahead of a healthcare deployment, Cortega needed a rigorous, evidence-based way to validate its own platform’s behavior against real agentic workflows rather than relying on one-off manual checks. Avahi designed and delivered a reusable evaluation framework that installs Cortega in front of a real, production-style AI agent workflow, drives realistic traffic through it, and captures the results as structured, traceable evidence, giving Cortega a tool it can keep running as its platform grows.
Cortega.ai is an AI governance and agentic-security platform built for regulated industries such as healthcare and financial services, where AI systems handling sensitive data need consistent authentication, policy enforcement, and observability across every model and tool call they make.
As Cortega’s platform matured, expanding across more LLM providers, agent frameworks, and deployment environments, the company needed a way to validate that growing connectivity with evidence, not one-off manual testing that would have to be redone by hand every time a new capability shipped. With a healthcare deployment on the horizon, Cortega needed to demonstrate, with data, that its governance layer correctly enforces policy and captures the right evidence under real conditions, and it needed a way to keep proving that as the platform kept changing.
Both the evaluation framework and the real-world agent workflows it tests against are built on AWS. Amazon Bedrock hosts the underlying language models that the test workflows call, Amazon EKS runs Cortega’s own gateway and control plane inside the evaluation sandbox, and Amazon Aurora backs the structured data used by the reference workflows under test. Running the entire evaluation, from agent to Cortega’s gateway to the underlying model and tool calls, inside AWS-managed infrastructure gave the framework a consistent, controlled environment to generate repeatable, trustworthy results.
As a premier-tier AWS partner with experience building and evaluating production agentic AI systems, Avahi brought both the AWS infrastructure expertise and the agent-framework fluency needed to stand up realistic test conditions rather than a synthetic benchmark. Cortega needed a partner who could build against real, actively maintained open-source agentic workflows instead of bespoke test scaffolding, and who could hand back a framework its own team could operate independently once the engagement closed.
Rather than building a synthetic test harness from scratch, Avahi installed real, actively maintained open-source AI agent workflows, spanning financial-services and healthcare use cases, as the foundation for realistic evaluation traffic. Avahi first deployed the reference workflow unmodified to establish a known-good baseline, then installed Cortega’s gateway and control plane in front of its language-model and tool traffic.
On top of that foundation, Avahi built an evaluation framework made of independently swappable layers: a library of test scenarios covering policy enforcement, benign traffic, and tool-level observability; a traffic-execution layer that drives that traffic through the workflow; an evidence-correlation layer that maps each scenario to Cortega’s own telemetry records; and a scorecard and coverage-matrix generator that turns the results into a reviewable report. Because each layer is versioned and swappable, Cortega can extend the framework to new workflows, providers, or tool integrations later through configuration rather than rebuilding it.
The framework also produced a benchmark suite characterizing gateway throughput and latency, the added overhead of routing tool calls and guardrail checks through Cortega, and end-to-end overhead across a full multi-step agent run, along with practical guidance for scaling the platform under production load. Avahi handed the complete framework off as a containerized repository, and walked Cortega’s technical team through re-running it and extending it to a new test scenario independently.
Cortega came out of this engagement with an evaluation capability it did not have before: a repeatable, evidence-based way to validate its own governance platform against realistic agent traffic spanning both healthcare and financial-services scenarios, with every result traceable back to Cortega’s own telemetry rather than resting on manual spot checks. The framework’s modular design means Cortega’s team can extend it to new providers, agent frameworks, or workflows going forward without rebuilding it, and the benchmark results give Cortega a documented basis for scaling and capacity planning as it moves toward production deployment.
Cortega.ai
Maryland, USA
AI Governance / Agentic Security
Amazon Bedrock, Amazon EKS, Amazon Aurora (PostgreSQL), Amazon VPC, OpenTelemetry
Cortega.ai builds an AI governance platform that authenticates, inspects, blocks, redacts, and routes AI traffic, including LLM calls, agent traffic, and MCP tool calls, before it leaves a customer’s infrastructure boundary. Ahead of a healthcare deployment, Cortega needed a rigorous, evidence-based way to validate its own platform’s behavior against real agentic workflows rather than relying on one-off manual checks. Avahi designed and delivered a reusable evaluation framework that installs Cortega in front of a real, production-style AI agent workflow, drives realistic traffic through it, and captures the results as structured, traceable evidence, giving Cortega a tool it can keep running as its platform grows.
Cortega.ai is an AI governance and agentic-security platform built for regulated industries such as healthcare and financial services, where AI systems handling sensitive data need consistent authentication, policy enforcement, and observability across every model and tool call they make.
As Cortega’s platform matured, expanding across more LLM providers, agent frameworks, and deployment environments, the company needed a way to validate that growing connectivity with evidence, not one-off manual testing that would have to be redone by hand every time a new capability shipped. With a healthcare deployment on the horizon, Cortega needed to demonstrate, with data, that its governance layer correctly enforces policy and captures the right evidence under real conditions, and it needed a way to keep proving that as the platform kept changing.
Both the evaluation framework and the real-world agent workflows it tests against are built on AWS. Amazon Bedrock hosts the underlying language models that the test workflows call, Amazon EKS runs Cortega’s own gateway and control plane inside the evaluation sandbox, and Amazon Aurora backs the structured data used by the reference workflows under test. Running the entire evaluation, from agent to Cortega’s gateway to the underlying model and tool calls, inside AWS-managed infrastructure gave the framework a consistent, controlled environment to generate repeatable, trustworthy results.
As a premier-tier AWS partner with experience building and evaluating production agentic AI systems, Avahi brought both the AWS infrastructure expertise and the agent-framework fluency needed to stand up realistic test conditions rather than a synthetic benchmark. Cortega needed a partner who could build against real, actively maintained open-source agentic workflows instead of bespoke test scaffolding, and who could hand back a framework its own team could operate independently once the engagement closed.
Rather than building a synthetic test harness from scratch, Avahi installed real, actively maintained open-source AI agent workflows, spanning financial-services and healthcare use cases, as the foundation for realistic evaluation traffic. Avahi first deployed the reference workflow unmodified to establish a known-good baseline, then installed Cortega’s gateway and control plane in front of its language-model and tool traffic.
On top of that foundation, Avahi built an evaluation framework made of independently swappable layers: a library of test scenarios covering policy enforcement, benign traffic, and tool-level observability; a traffic-execution layer that drives that traffic through the workflow; an evidence-correlation layer that maps each scenario to Cortega’s own telemetry records; and a scorecard and coverage-matrix generator that turns the results into a reviewable report. Because each layer is versioned and swappable, Cortega can extend the framework to new workflows, providers, or tool integrations later through configuration rather than rebuilding it.
The framework also produced a benchmark suite characterizing gateway throughput and latency, the added overhead of routing tool calls and guardrail checks through Cortega, and end-to-end overhead across a full multi-step agent run, along with practical guidance for scaling the platform under production load. Avahi handed the complete framework off as a containerized repository, and walked Cortega’s technical team through re-running it and extending it to a new test scenario independently.
Cortega came out of this engagement with an evaluation capability it did not have before: a repeatable, evidence-based way to validate its own governance platform against realistic agent traffic spanning both healthcare and financial-services scenarios, with every result traceable back to Cortega’s own telemetry rather than resting on manual spot checks. The framework’s modular design means Cortega’s team can extend it to new providers, agent frameworks, or workflows going forward without rebuilding it, and the benchmark results give Cortega a documented basis for scaling and capacity planning as it moves toward production deployment.
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