Sources
Tenant access
Document provenance
Retrieval boundary
AI Agents & Custom Applications
An AI agent is useful only when it knows what it can do — and what it cannot.
Build applications around a useful task, permitted evidence and bounded tools. Make uncertainty and escalation part of the product.
USER REQUESTPrepare an answer from permitted records
Tools
Allowed actions
Input validation
Failure recovery
Human approval
Customer commitments and consequential writes stay with an authorized person.
Operations / Product / Technical leads
The engagement
What changes. What you receive. How we check it.
What changes
A clearly scoped improvement
A scoped application, tool permissions, representative evaluation cases, failure handling and operating documentation.
What to measure
Compare before and after
Measure task acceptance, escalation rate, failure recovery and cost per accepted result.
How we start
One owner, one workflow
Bring a representative example. Together we confirm scope, access, acceptance criteria and pricing before implementation.
ExploreCurrent vs controlled / Reference workflow
The same work. A clearer route through it.
StageCurrent / frictionControlled / accountable
01Request
CurrentAccept an ambiguous request
ControlledConfirm the task and user authority
02Evidence
CurrentRetrieve documents without a clear scope
ControlledRetrieve permitted records with sources
03Model
CurrentTreat fluent output as correct
ControlledDraft within a defined response contract
04Tools
CurrentExpose broad write permissions
ControlledAllow only scoped actions
05Validation
CurrentPass unverified output downstream
ControlledValidate outputs and stop on missing evidence
06Human
CurrentAsk a person only after damage
ControlledRequire approval for accountable decisions
Your team approves consequential writes, customer commitments, data access and exceptions.
Interactive reference / No external action
Test the boundary before trusting the action.
Reference verdict
HUMAN REVIEW
The checks pass in this fictional example. The consequential action still needs approval.
Automation / AI / Your team
Give each kind of work the right owner.
Automation
Moves and checks
Enforce permissions, schemas, state transitions, budgets and audit events.
AI assistance
Interprets and prepares
Retrieve, interpret, classify and draft within the permitted task and source scope.
Human accountability
Approves and decides
Your team approves consequential writes, customer commitments, data access and exceptions.
Modelled impact / Calculator
What does the retry loop add to the bill?
Illustrative assumptions / editable
Rates are your own assumptions. A gateway limit covers only calls routed through it. Quality and human review require separate acceptance checks.
Use this scenario in my assessment ↗Formula and sensitivity
Base monthly cost = daily runs × calls per run × cost per call × operating days. Total = base × (1 + extra retry calls / 100). This is a linear scenario, not a token or provider billing simulator.
Practical starting points
Start small enough to verify.
Bounded workflow
Knowledge assistant
Inspect request: Accept an ambiguous request. Agree the owner and acceptance evidence before changing the live process.
ExploreBounded workflow
Document review workflow
Inspect evidence: Retrieve documents without a clear scope. Agree the owner and acceptance evidence before changing the live process.
ExploreBounded workflow
Operations copilot
Inspect model: Treat fluent output as correct. Agree the owner and acceptance evidence before changing the live process.
ExploreExternal guidance / Separate from our work
Use published risk guidance as a reference.
EXTERNAL INDUSTRY EVIDENCE
NIST AI Risk Management Framework
A voluntary framework for incorporating trustworthiness into AI design, development, use and evaluation. This is external guidance, not certification of a LaunchSoloAI build. Reviewed 2026-09-12.
ExploreEXTERNAL INDUSTRY EVIDENCE
OWASP: Excessive Agency
OWASP describes risk from excessive functionality, permissions and autonomy. The reference informs boundary design; it does not prove any implementation is secure. Reviewed 2026-09-12.
ExploreLaunchSoloAI engineering evidence
Inspect the artifact and its boundary.
DELIVERED TECHNICAL ARTIFACT
Apollo Finvest
A delivered take-home build demonstrates tool and provider architecture. It is not a financial-services production claim.
Open the evidence record- 01User mode
Anonymous and signed-in modes
- 02Tool routing
Bounded tool selection and audit record
- 03Provider fallback
Handle provider failure explicitly
Decision owner
Take-home implementation record. This explanatory diagram is not a production deployment screenshot.
Implementation / Evidence before expansion
A bounded engagement, with a decision at each stage.
- 01
Map
Confirm the workflow, owner and baseline.
- 02
Bound
Agree data, permissions, scope and unacceptable failures.
- 03
Build
Implement the smallest useful intervention.
- 04
Verify
Measure task acceptance, escalation rate, failure recovery and cost per accepted result.
- 05
Hand over
Train the owner, document recovery and decide whether to expand.
What you receive
A scoped application, tool permissions, representative evaluation cases, failure handling and operating documentation.
A practical next step
Bring one workflow. Find the next useful move.
Start with the process, the current tools and the person who owns the next decision. No system access needed.
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