Agentic ETL Pipelines
Extract fields, apply configured validation rules, hold uncertain records for review, and retain links to the source documents.
View the capabilityThinkStack for one governed AI estate
Bring your AI work under one set of controls.
Many regulated companies now run AI pilots in several tools, each with its own contract, security review, and audit gaps. ThinkStack provides one place to build, run, and govern agents, so access, policy, cost, and evaluation are managed once instead of once per vendor.
The work to improve
Count what you run before you add another tool.
Each new AI tool brings its own contract, its own connectors and credentials, and its own security review. Nobody can say which agents exist across the tools, what data they touch, or what they cost together. Moving the work onto one governed runtime will not remove every tool, but it gives the business one place to set policy and one record to inspect.
01 · Inputs your team can access
AI inventoryTools, agents, owners, and the data each touches
Contracts and spendWhat each vendor costs and when it renews
Security requirementsAccess, data handling, and review criteria
Existing systemsConnectors, credentials, and tools agents need
ThinkStack
Configured agents, tools, and workflow steps
Agent Harness BuilderCompose agents from models, prompts, tools, MCP servers, and guardrails. Each version is tracked and can be compared with the last.Agent Runtime EnvironmentsRun agent code in sandboxed, isolated runtimes with governed dependencies, and reuse them across chat and agent harnesses.Hosted MCP ServersDescribe a tool in plain language or register an existing MCP server, and verify it before agents are allowed to call it.Teams & Agent SharingShare agents, knowledge bases, guardrails, tools, flows, and pipelines with a person, a team, or the whole organization.Policy & GuardrailsConfigure and test runtime rules. Inspect which rule applied, the text that matched, and the action recorded.Observability & TracingInspect tool calls, source retrievals, generated responses, errors, and usage costs for each run.
Defined access · Configured checks · Accountable reviewers
02 · Outputs for review
Estate inventoryEvery agent, owner, and data source in one listMigration planWhich workflows move first, and what staysUsage and cost viewSpend by agent, team, and model
Where to start
Three places to start
Choose the overlap that costs you the most today. These workflows show the pattern; what can move depends on each tool's role, your contracts, and your security review.
01Inventory and overlap reviewList what runs today and where tools overlap.
- Start with
- Contracts, usage reports, and a list of AI tools and agents from each team.
- Agent preparation
- Extract the details from contracts and reports, and prepare a table of tools, owners, data touched, and spend, with the source for each row.
- What your team receives
- An inventory with sources, and a list of overlapping capabilities.
- Human review and approval
- The technology sponsor confirms the list and decides which overlaps matter.
- What to measure
- Tools identified, share of spend traced to a named owner, and items found missing later.
02Rebuild one workflow on a shared runtimeMove a single workflow and compare it with the original.
- Start with
- One existing agent workflow, its prompts, its tools, and its test cases.
- Agent preparation
- Recreate the workflow with a managed harness, connect its tools through registered MCP servers, run it in an isolated runtime, and apply your guardrails.
- What your team receives
- A governed version of the workflow with its version history and test results.
- Human review and approval
- The workflow owner compares outputs with the current tool on the same cases, and security reviews access.
- What to measure
- Output quality against the current tool, setup effort, and time to complete the security review.
03One view of usage, cost, and policyAnswer "what is running and what does it cost?" from one record.
- Start with
- Traces and usage records from agents running on the platform.
- Agent preparation
- Summarize runs, tool calls, errors, and usage cost by agent, team, and model, and show which guardrails applied to each run.
- What your team receives
- A usage and cost report by agent and model, with the traces behind it.
- Human review and approval
- Finance and the technology owner check the attribution and decide what to retire or renegotiate.
- What to measure
- Spend attributed to an owner, agents with a named reviewer, and time to answer the question.
Review example
Read one entry in the AI inventory
Each agent has an owner, a data scope, the controls applied to it, and a decision about what happens next.
- Agent
- Intake summarizer (fictional)
- Owner
- Operations, with a named lead
- Data touched
- Application forms; no health records
- Controls applied
- Personal-data masking and a topic restriction
- Next step
- Move to the shared runtime next quarter
The technology sponsor decides which tools to keep, move, or retire. Moving a workflow depends on integration scope, validation for its intended use, and your security review.
Pilot measurement
Count the costs on both sides.
Compare the full cost of running a workflow today with its cost on the shared platform, including migration effort, review time, and platform fees. A shorter vendor list is a result only if total cost and control both improve.
- Vendors and contracts
- Number in scope, and their renewal dates.
- Security reviews
- Reviews needed per tool, and time to complete each.
- Cost attribution
- Share of spend traced to an agent and an owner.
- Migration effort
- Time to rebuild and validate each workflow.
Platform capabilities
The modules behind these workflows
Review the relevant capabilities with your technical team and confirm the configuration for the intended use.
Data Analysis Playground
Run SQL and Python analyses, then inspect the code, tables, charts, and execution records.
View the capabilityAgent Harness Builder
Compose agents from models, prompts, tools, MCP servers, and guardrails. Each version is tracked and can be compared with the last.
View the capabilityAgent Runtime Environments
Run agent code in sandboxed, isolated runtimes with governed dependencies, and reuse them across chat and agent harnesses.
View the capabilityHosted MCP Servers
Describe a tool in plain language or register an existing MCP server, and verify it before agents are allowed to call it.
View the capabilityObservability & Tracing
Inspect tool calls, source retrievals, generated responses, errors, and usage costs for each run.
View the capabilityTeams & Agent Sharing
Share agents, knowledge bases, guardrails, tools, flows, and pipelines with a person, a team, or the whole organization.
View the capabilityRelated
Explore related pages
See how this outcome connects to the work of other teams and industries.
Common questions
Before you plan the workflow
Will one platform replace all our AI tools?
Not necessarily. Start with the workflows where overlap, cost, or control gaps are greatest. Some tools will stay because they serve a purpose the platform does not.
What does moving a workflow involve?
Rebuilding its prompts and tools on the platform, connecting its data sources, and testing it against the current tool's output on the same cases. Plan for integration and validation time.
Where does it run?
Deployment options, including isolated runtimes, depend on your environment. We will confirm what fits your security requirements before you plan a migration.
How do we start?
List what you run today, then pick one workflow with an overlap you can measure. We will review the sources, access, and review steps with your technology and security owners.