AI Engineering
We build agentic AI systems that understand your business, act across your tools, and run under enterprise governance. Designed like production systems — because that's what they become.
How much autonomy does the problem need?
Not every problem needs an agent. We map the work first, then use the least autonomy that solves it — every step up adds capability and adds failure modes.
The human does the work
AI removes friction from work people are already doing.
- Knowledge assistants with permission-aware document retrieval
- Code copilots and review assistants for engineering teams
- Drafting, summarization, and synthesis
- Search and Q&A across structured and unstructured data
Bottleneck: access to information, or repetitive cognitive work.
Human: in every loop.
The AI runs a known process
The path is known; the model handles the parts that need judgment — classify, extract, draft, route. Deterministic where it can be, probabilistic only where it must be.
- Document and email triage, extraction, and routing
- Data enrichment, validation, and reconciliation
- Multi-system workflows across CRM, ERP, and databases
- Approval gates on anything irreversible
Bottleneck: manual handoffs and repetitive process steps.
Human: on exceptions and approvals.
The AI pursues an outcome
The path isn't known in advance. The agent chooses its next step toward a defined outcome, within explicit bounds on what it may touch and what it must escalate.
- Agents that research, validate, and act on operational data
- Multi-step execution where the sequence depends on findings
- Tool access through governed connectors
- Explicit bounds, escalation rules, and kill switches
Bottleneck: coordination and decision latency across systems.
Human: on bounds, approvals, and escalation.
If your problem doesn't need an agent, we'll tell you — before the proposal, not after.
An agent is code with a non-deterministic branch.
Everything else is the same engineering you already expect: version control, tests, deploys, rollback, monitoring, cost. The one difference is that the branch is chosen by a model instead of an if statement.
That single change is why an agent needs evals instead of unit tests and guardrails instead of input validation. It's not a new discipline — it's an old discipline with a new class of failure. We treat prompt design and agent architecture as software engineering, because that's what they are.
Here is a brief example of how refining a prompt works. Each answer is sent to an LLM along with everything you have answered so far, so the prompt gets sharper with every step.
Want some help making this agent a reality? Share this prompt with us.
Creating agents starts with understanding the business.
The model is the last decision, not the first. We map the real work, anchor it to an outcome you can measure, then design the agent around your systems — and stay on it after release.
Shadow operations, interview stakeholders, and trace the decisions, handoffs, and data flows where an agent can create measurable value.
- Process mapping
- Stakeholder interviews
- Data & system inventory
Every agent is tied to a business outcome — faster quotes, fewer escalations, cleaner data — with clear success metrics and guardrails.
- Success & evaluation criteria
- Risk & compliance review
- Human-escalation rules
We design perception, reasoning, tools, memory, and handoff points so the agent behaves predictably inside your existing enterprise systems.
- Agent architecture
- Tool & API design
- RAG & knowledge grounding
Agents ship with monitoring, versioning, kill switches, and continuous evaluation — then get tuned against real production outcomes.
- Staged rollout & rollback
- Kill switches & escalation paths
- Continuous evaluation & observability
Production outcomes feed back into design.
How the stack fits together.
Bedrock AgentCore is the agent runtime — where the agent reasons, remembers, calls tools, and gets evaluated. n8n is the control plane — when agents run, who approves, what happens when something fails. Governance wraps both.
- Workflow triggers
- Human approvals
- Retries & logs
- Escalation
Retrieve context and current state
Choose the next action within policy
Call a tool, write, or escalate
Record the outcome, evaluate, adjust
AWS Bedrock AgentCore
Where the agent runs: managed runtime, tool access through an MCP gateway, scoped identity, persistent memory, and evaluation hooks — inside your VPC, under your IAM.
- Managed runtime for long-running, multi-step agents
- Tool access via MCP gateway rather than bespoke integrations
- Scoped agent identity, guardrails, and policy enforcement
- Model choice across Amazon, Anthropic, and third-party models
n8n
When agents run, who approves what, and what happens when something fails.
- Visual workflow design for triggers and multi-step routing
- Human-in-the-loop approvals and exception handling
- Connections to CRM, ERP, databases, and messaging platforms
- Execution logs, retries, and operational dashboards
Tooling & integration
Agents reach your systems through governed connectors, not direct credentials.
- MCP servers wrapping internal APIs and databases — written once, reusable by any agent or host
- RAG and knowledge-base grounding with permission-aware retrieval
- Event-driven and scheduled triggers
- Fallback paths when a tool fails or confidence is low
Built for enterprise control.
Agent deployments inherit the same security, access, and audit standards as any production system — with additional controls for autonomous behavior and fleet-scale operations.
Governance and security checklist.
Enterprise agent deployments require control by design. This checklist is where every engagement starts.
Agents operate with the least privilege required, authenticated through enterprise identity systems.
- IAM / SSO integration for every agent and operator
- Role-based permissions scoped to tools, data, and actions
- Secrets stored in vaults, never hard-coded in workflows
- Multi-factor authentication for admin and deployment access
- Regular access reviews and automated offboarding
Every decision, tool call, and data access event is logged, attributable, and exportable for review.
- CloudTrail and execution logs for all agent actions
- Immutable audit history with user and agent attribution
- PII and sensitive-data handling with guardrails and masking
- SOC 2 and GDPR-aligned controls where required
- Periodic policy reviews and compliance reporting
Agents are managed as a coordinated fleet with versioning, observability, and safe operational controls.
- Centralized agent registry with version control and ownership
- Deployment pipelines with staging, canary, and rollback
- Real-time monitoring, cost attribution, and usage quotas
- Kill switches and circuit breakers for high-risk actions
- Human-in-the-loop approvals for exceptions and escalations
We assume the agent will be wrong.
Every action an agent can take is reversible, rate-limited, or gated by a human — chosen per action, not per agent. Anything that can't be undone doesn't get automated.
Every agent is built with identity, audit, and operational controls from day one — not bolted on after deployment.
How we build AI systems.
Evaluation & Observability
All levelsShip AI systems with confidence using continuous evaluation, guardrails, and production monitoring.
- Offline and online evaluation frameworks
- Hallucination, toxicity, and safety guardrails
- Prompt versioning and A/B testing
- Tracing, cost attribution, and SLOs
Agent Discovery & Design
Automate · DelegateIdentify high-value agent opportunities and design agents that fit your operations, constraints, and compliance requirements.
- Business-process mapping and value-case definition
- Agent architecture: perception, reasoning, tools, guardrails
- Risk assessment and human-escalation design
- Prototype scoping for a production pilot
Agent Development on AWS
DelegateImplement agents on AWS Bedrock with the right models, knowledge bases, action groups, and enterprise security controls.
- Bedrock AgentCore: runtime, gateway, identity, and memory
- Model evaluation and selection for each use case
- Guardrails, IAM policies, and VPC networking
- Integration with CloudWatch, CloudTrail, and enterprise SSO
Workflow Automation & Control Plane
AutomateOrchestrate agent workflows, triggers, approvals, and monitoring through n8n so operations teams can manage agents without writing code.
- Visual workflow design and node integration
- Scheduled, event-driven, and webhook triggers
- Human-in-the-loop approvals and notifications
- Execution logs, error handling, and retries
Retrieval-Augmented Generation
Assist · Automate · DelegateConnect language models to your documents, databases, and APIs with secure, observable RAG pipelines.
- Vector search with pgvector, Pinecone, or Weaviate
- Chunking, embedding, and reranking strategies
- Source attribution and citation chains
- Permission-aware retrieval for enterprise data
Most engagements start the same way: one agent, one outcome, four to six weeks to a production pilot.
Our stack.
Grouped by the job each layer does.
n8n when the steps are known and humans approve. LangGraph when the flow needs an explicit state graph with rollback points. AgentCore when the agent needs managed identity, memory, and evaluation.
Deployment, infrastructure-as-code, and platform observability → Cloud Platforms · DevOps & Infrastructure
Start with a focused agent engagement.
Most teams begin with a production pilot: one agent mapped to one business outcome. We design it, deploy it on AWS Bedrock or n8n, and scale from there.
Book a discovery call.
Tell us what you're exploring. We'll reply within one business day with next steps and a calendar link.
We build Exceptional.
Whether creating new software platforms, codifying business logic into Agentic workflows and Agents, or an enterprise system that needs maintaining, we can help. Professionally engineered, documented, and maintained.