Experience the engineering practices that converge to build AI teams. Explore our core engineering standards.
From AI-Blended Development to Shift-Right validation, explore how our core pillars converge to drive engineering excellence while adhering to the OpenAGI philosophy of openness and trust.
Human-AI Pair Programming at Scale
AI-Blended development is our fundamental reimagining of the software creation process. We move away from manual 'typing' towards a model of 'technical coaching' where AI handles the implementation heavy-lifting while humans provide the strategic intent.
By leveraging AI for specification analysis, architecture design, and automated testing, we reduce cognitive load on engineers and allow them to focus on high-level system logic. This creates a state of flow where the 'Spec' becomes the primary driver of development, and the AI acts as a highly capable implementation partner.
This approach doubles our delivery velocity while maintaining a 0% regression rate on core logic, as human oversight is concentrated where it matters most: at the design and verification boundaries.
Accelerates discovery and implementation phases, reducing the 'Time-to-Quality' and minimizing manual boilerplate costs across the project lifecycle.
Aligns with 'Open Training' by documenting architectural decisions and hyperparameters as part of the spec-driven coaching loop.
We feed detailed requirements into AI agents that generate technical blueprints, which are then reviewed and 'coached' by senior architects.
AI agents generate initial Pull Requests, including comprehensive doc-strings and inline commentary explaining the 'Why' behind the implementation.
We use AI to recursively refactor codebases, identifying opportunities for abstraction and performance optimization.
Draft high-level Spec in OpenSpec format
Run AI Spec-Analyzer to find logical gaps
Generate initial implementation via Agentic Coding
Senior Human Review of AI-generated PRs
Client-Embedded Development
Our FDE model eliminates the 'lost in translation' effect common in enterprise software. By embedding engineers directly into the business environment, we bridge the gap between technical possibility and business reality.
FDEs sit with users to map actual workflows, identify hidden pain points, and prototype solutions in real-time. This immersion allows them to identify 'shadow workflows'—those informal, undocumented processes that are often the real bottlenecks.
The FDE model ensures that AI agents and systems are perfectly aligned with domain-specific needs. This embedding creates a feedback loop that is measured in hours, not weeks.
Minimizes 'Rework Debt' by ensuring high-fidelity discovery. Correct requirements from Day 1 prevent expensive downstream architectural shifts.
Supports 'Accountability' by embedding engineers who take end-to-end ownership of the business outcome, not just the code.
FDEs spend up to 50% of their initial time observing users in their natural environment to understand the 'unspoken' requirements.
We use 'White-Label' UI frameworks and synthetic data to put working tools in users' hands within days.
FDEs act as the technical lead for the client, managing the integration of AI agents into existing legacy systems.
Observation and mapping of the current 'as-is' state.
Daily prototype cycles with live user feedback.
Hardening the solution for enterprise-wide deployment.
Scaling Development Velocity
Platform Engineering is our answer to the 'Cognitive Load' problem. We build an Internal Developer Platform (IDP) that offers 'Golden Paths'—pre-vetted, secure, and fully automated routes to production.
If a developer needs a new microservice, they don't file a ticket; they use a self-service CLI that provisions the repo, CI/CD pipeline, monitoring, and security guardrails in under 5 minutes.
We treat our internal platform as a product, with the engineer as the customer. This enables product teams to deliver value faster while maintaining consistent organizational standards.
Reduces infrastructure sprawl and operational 'Toil.' Standardized paths lower the TCO of the entire engineering environment overhead.
Enables 'Supply Chain Transparency' (SPDX 3.0) by automatically generating AI Bill of Materials (BOM) for every provisioned service.
Standardized templates for Service, DB, and AI infrastructure that come out-of-the-box with compliant defaults.
We replace manual approval gates with automated guardrails that prevent non-compliant code from even being built.
A centralized portal where engineers manage their own infrastructure throughout the lifecycle.
Moving Quality Gates Upstream
Shift-Left is our commitment to 'Software Quality at the Source.' We believe that a bug found in the IDE is a minor task, while a bug found in production is a crisis.
Our Shift-Left engine integrates linting, unit testing, security scanning, and architectural linting directly into the developer's local environment. This ensures that quality is built in, not bolted on.
By the time a PR is opened, the code has already passed 90% of our quality and security checks, leading to significantly higher confidence in every release.
The most effective way to lower long-term maintenance costs. Fixing a defect upstream is up to 100x cheaper than post-deployment remediation.
Supports 'Safety & Governance' by enforcing security and compliance checks during the development phase.
We provide custom IDE extensions that give real-time feedback on security risks and architectural anti-patterns.
Production deployments are contingent on a 'Green Build' that includes security and performance audits.
Validating in the Real World
While Shift-Left catches known defects early, Shift-Right ensures that systems behave as expected under real-world conditions. This practice involves moving testing and validation directly into production.
Our Shift-Right approach leverages feature flags, canary deployments, and chaos engineering to safely test how our AI agents handle unexpected loads and failure modes.
This is particularly vital for AI agents where 'correctness' can be subjective. We monitor for 'model drift' and 'semantic misalignment' in real-time.
Protects business value post-launch. Minimizes the cost of downtime and service degradation by catching issues before they impact the broader user base.
Directly implements 'Continuous Monitoring' for model drift and user feedback integration as defined in our operations pillar.
We use Canary deployments and Blue/Green strategies to limit the blast radius of new updates.
We intentionally inject failures to ensure our AI agents can fail gracefully and maintain system stability.
Deep instrumentation that captures exactly how users interact with our AI interfaces.
Built-In Visibility
ODD is the practice of 'Developing for Debugging.' We don't view logging and metrics as an afterthought; we view them as a primary requirement. If a system isn't observable, it's not production-ready.
We use the 'Three Pillars of Observability'—Metrics, Logs, and Traces—to create a high-definition map of our system's health.
ODD is crucial for complex agent meshes where failure modes are often non-linear. It answers 'Why' a system is behaving a certain way, not just 'If' it is down.
Reduces Mean Time to Repair (MTTR), drastically lowering the operational cost of managing complex microservice and agent environments.
Fosters 'Transparency' by providing real-time serving metrics and content filtering visibility.
Feature work is automatically deprioritized in favor of reliability work if our error budget is exceeded.
AI-powered monitoring that flags 'abnormal' behavior before a human-defined threshold is ever crossed.
Contracts Before Implementation
API-First is our strategy for organizational decoupling. We treat our APIs as formal products. By defining the 'Contract' first, we allow multiple teams to work in parallel without blocking each other.
This is essential for building an 'Agent Mesh' where multiple specialized AI agents communicate via structured APIs. The contract is our 'Source of Truth.'
This contract-driven approach enforces rigid service boundaries, reduces integration debt, and ensures that our systems are inherently modular.
Prevents 'Distributed Monolith' costs. Modular, API-first systems are significantly easier and cheaper to scale and refactor.
Promotes 'Interoperability & Open Tooling' by providing clear, documented interfaces for internal and external consumers.
No development begins until the API spec is peer-reviewed and published to our central Registry.
Our platform automatically spins up mock endpoints based on every API spec version for parallel integration testing.
These practices don't work in isolation. They converge to create a seamless engineering engine that optimizes TCO while upholding our philosophy of transparent and accountable AI.
Our FDEs use AI-Blended toolsets to prototype custom enterprise solutions with unprecedented speed and precision.
We secure the 'Left' during development and validate resilience on the 'Right' in production for end-to-end reliability.
Our platform provides the self-service APIs that allow product teams to scale without integration friction.
Detailed observability is the lifeblood of monitoring and optimizing autonomous AI agents in real-world scenarios.
Our engineering practices are directly mapped to our Total Cost of Ownership (TCO) framework to ensure maximum value delivery.
Platform Engineering minimizes structural debt and infrastructure sprawl.
FDE and AI-Blended development accelerate validation and capture requirements correctly.
Shift-Left and API-First ensure high-quality code and modular architecture.
Shift-Right and ODD manage operational costs and ensure system reliability.
Our engineering standards are built on the foundations of transparency, safety, and accountability.
Spec-Driven Development and API-First ensure a transparent 'Idea-to-Code' journey.
Shift-Left and gated CI/CD stages enforce rigorous safety and fairness standards.
Shift-Right and ODD provide the monitoring and accountability needed for live AI systems.
Adopt the engineering practices that optimize TCO and uphold the highest standards of AI transparency. Let's build accountable systems together.