Technical Overview
How we design and operate AI workflow systems in production
This page is for technical stakeholders who want to understand how our systems are designed, what problems we optimize for, and why this architecture survives real-world use.
If you're looking for a lightweight demo or prompt orchestration examples, this will feel heavy by design.
The architecture described here is designed to be evolved and hardened alongside early client workflows, not shipped as a fixed abstraction.
Design Principles
Non-negotiable
We design AI systems under the assumption that:
- Inputs will be messy
- Users will behave unpredictably
- Models will fail silently
- Costs will grow non-linearly
- Someone will eventually ask "why did the system do this?"
Everything else flows from that.
Conceptual Architecture
The system is designed as a workflow runtime, not a collection of AI calls.
Client / UI
Web, Admin, Operators
Workflow API Layer
Auth, Validation, WAF
Workflow Execution Engine
AI Providers
LLMs, Embeddings
Human Review / Ops
• Approval gates
• Escalation
• Decision capture
Data & Document Pipelines
State, Audit & Observability
How to Read This Diagram
Top → bottom is the execution flow
Left → right shows where decisions branch
AI is a dependency, not the center
Humans are explicit participants, not exceptions
State and auditability are first-class outputs
This structure is what allows the system to:
Pause
safely
Resume
intelligently
Fail
without cascading
Explain
itself later
Why This Matters
Most AI systems look like this
Ours looks like this instead
That difference is what makes automation trustworthy in real operations.
AI as a Workflow Runtime
Not a chain
We do not treat AI as a single request/response interaction.
Instead, AI execution is modeled as a stateful workflow:
- Explicit nodes with defined responsibilities
- Controlled state propagation between steps
- Deterministic transitions where possible
- Human-interruptible execution points
- Resumable workflows rather than one-shot runs
This allows:
- Partial retries without re-running everything
- Targeted debugging of failures
- Controlled escalation to humans
- Safe evolution of workflows over time
In practice, this looks much closer to a distributed workflow engine than an "agent."
Human-in-the-Loop by Architecture
Human review is not an afterthought or a UI toggle.
It is a first-class execution state.
We design workflows so that:
- The system knows when it is not allowed to proceed
- Human decisions are recorded as state transitions
- Downstream steps can depend on explicit approval
- Automation never silently bypasses judgment
This is critical in domains where:
- Commitments matter
- Language can be interpreted later
- AI output may be quoted or relied upon
Document & Data Handling
Reality, not demos
Most failures happen at ingestion, not generation.
Our systems include:
- Secure direct-to-storage uploads
- Event-driven document pipelines
- Asynchronous processing with backpressure
- Explicit failure queues and retries
- Bounded resource usage and size limits
This prevents:
- Runaway memory usage
- Stuck workflows
- Silent partial failures
- Cost explosions during spikes
Documents are treated as inputs to workflows, not blobs handed to a model.
Model Orchestration, Not Model Worship
We assume:
- Models will change
- Providers will change
- Costs will change
- Performance will drift
So systems are designed to:
- Route between models intentionally
- Isolate model-specific behavior
- Support multiple providers
- Fail safely when models degrade
- Cap cost and token exposure per execution
Models are dependencies — not the architecture.
Auditability & Post-Hoc Analysis
Every system we build assumes someone will ask:
"What did the AI see, decide, and produce — and why?"
We design for:
- Traceable execution paths
- Prompt and context archival (with redaction)
- Versioned workflow definitions
- Explicit state snapshots
- Replayable failure scenarios
This is what makes systems defensible in regulated or high-risk environments.
Infrastructure Philosophy
We build on AWS-native primitives and managed services, with a bias toward:
- Event-driven architectures
- Explicit permissions and blast-radius control
- Environment-safe deployments
- Cost-aware defaults
- Minimal always-on infrastructure
Infrastructure choices are made to:
- Scale down as well as up
- Fail loudly rather than silently
- Be understandable six months later
We optimize for operability, not novelty.
Security & Safety Posture
By default:
- Secrets are never embedded in code
- Storage is encrypted at rest and in transit
- Access is role-scoped, not blanket
- External integrations are verified and signed
- Public endpoints are intentionally constrained
Security is treated as a system property, not a checklist.
What This Architecture Is Good At
- Long-running workflows
- Document-heavy operations
- Decision-support systems
- AI with legal, financial, or reputational impact
- Gradual automation with human oversight
- Evolving workflows without rewrites
What This Architecture Is Not For
- Throwaway prototypes
- Viral consumer apps
- Purely generative toys
- "Agent swarms"
- Systems where failure is inconsequential
We're opinionated because production systems require it.
Engagement Expectations
Technical reality
When we work together, expect:
- Strong opinions, weakly held
- Architectural tradeoff discussions
- Explicit constraints and boundaries
- Resistance to unsafe shortcuts
- Systems designed to outlive initial enthusiasm
We take responsibility for what we ship.
If you want to sanity-check an AI system you're planning — or understand why an existing one feels fragile — we're happy to talk.