Shuttle to the Agent Stack
Stack Shuttle moves teams onto the agent stack and gets AI agents doing real work in production
- Workflows
- Evals
- Observability
- Orchestration
- Tools
- Context
- Memory
- Models
- Identity
- Guardrails
Four routes to the agent stack
Strategy, systems, operations, and foundations. Board one, or ride all four.
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↗
Agent Strategy
Workflow audit · Agent roadmap · Investment case
Find the workflows worth handing to agents. Define scope, autonomy levels, and the investment case.
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↗
Agent Systems
Architecture · Tools & context · Build · Integrate
Build agents wired into your data, tools, and platforms, with orchestration, retrieval, and tool access designed in.
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↗
Agent Operations
Evals · Observability · Human review · Enablement
Run agents like production software: evals, observability, cost control, and human review where it matters.
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↗
Agent Foundations
Context & data · Permissions · Platform · Guardrails
Get data, identity, permissions, and platform choices ready for agents that act on your behalf.
Where teams board
Most teams arrive with one of four problems. Each one has a first route.
- Agent pilots stuck in demo Evals and integration that get one agent into production Systems · Operations
- Leadership wants an agent plan A ranked audit of the workflows worth handing to agents Strategy
- Every team building its own agents A shared platform, permissions model, and build patterns Foundations · Systems
- Agents live, but nobody trusts them Evals, tracing, and human review that earn trust Operations
Seven layers, one route map
An agent is only as good as the stack under it. We work every layer, from the model to the workflow it serves.
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Workflows
The jobs agents own, with clear scope, owners, and measures of success.
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Evals & observability
Agents tested against real tasks, with every run traced, costed, and monitored.
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Orchestration
Planning, routing, and multi-step control across models and tools.
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Tools & actions
Scoped, safe access to the APIs and systems agents act on.
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Context & memory
Retrieval over your data, documents, and history, kept fresh and permissioned.
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Models
The right model for each task, chosen on quality, latency, and cost.
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Identity & guardrails
Who an agent acts as, what it may touch, and what it must never do.
Pilot to production, clear checkpoints
A practical process that takes agents from a promising demo to dependable work.
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i.
Discover
Map your workflows, systems, data, and constraints. Pick the jobs agents should own first.
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ii.
Design
Shape the architecture: models, tools, context, autonomy, and where people stay in the loop.
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iii.
Deliver
Build, integrate, and evaluate against real tasks. Ship with guardrails and observability in place.
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iv.
Run & evolve
Operate, measure, and improve. Widen agent scope where it earns trust, retire what does not.
Bring the messy agent question.
We'll turn it into a route.
Which workflows to hand to agents, what to build, how to evaluate it, and what should wait.