Action through APIs
Tools, owners, permissions, and reversible outcomes are explicit.
AI AGENTS · TOOLS AND APIS
AI agent development services
We build governed agents that can use approved tools and business systems, not merely generate text. The first release covers one bounded workflow, least-privilege access, explicit approvals, and a versioned evaluation set.
01 · DIRECT ANSWER
Where to start
An AI agent fits a bounded workflow where the system selects approved tools and performs verifiable actions. Permissions stay minimal, people approve consequential actions, and evaluation covers the full agent loop.
02 · FIT
When this approach fits
Multi-step workflow
The system must gather context, choose an action, and verify its result.
03 · INPUTS AND OUTPUTS
Stage deliverables
Inputs for discovery
- Tool and API map
- Roles and approval model
- Success, failure, and adversarial cases
- Cost and time limits
- 01
Discovery
Workflow, data, risk, baseline, and explicit pilot decision criteria.
- 02
Pilot
One end-to-end flow, a versioned evaluation set, error log, and comparable measurement.
- 03
Production
Roles, access, monitoring, versions, incident procedures, and a validated operating boundary.
04 · SYSTEM
Architecture and integrations
Agent loop
Planning, tool calls, result validation, and stopping have explicit contracts.
Security boundary
Data, instructions, and tool output are untrusted; permissions and arguments are validated server-side.
05 · DECISION
Continue or stop gates
| Decision | Condition |
|---|---|
| Continue | Value and quality are supported by comparable evidence, while residual risk and TCO are acceptable to the workflow owner. |
| Narrow the scope | Value exists, but some actions, sources, or error classes require a smaller AI role. |
| Stop | Data is unavailable, the result cannot be observed, deterministic automation is better, or residual error cost is unacceptable. |
How the result is measured
- Quality
- A versioned set of real scenarios, error types and severity, abstention, and manual corrections.
- System behaviour
- Latency, availability, cost per workflow, integration failures, and drift after changes.
- Business workflow
- Comparison with the baseline: cycle time, manual touches, throughput, or another preselected measure.
Operations and total cost
- Observability
- Logs that minimise sensitive data, decision traces, alerts, and incident review.
- Change control
- Data, model, prompt, and integration versions pass the evaluation set and have a rollback path.
- TCO
- Discovery, integrations, model calls, infrastructure, monitoring, human review, and support all count.
Risks and limitations
- Data and rights
- Data scope, processing basis, provenance, storage, and access are established before a pilot.
- Rare high-cost errors
- Average quality does not hide critical classes; those use constraints, abstention, or human decision.
- Dependencies
- External models and APIs can change price, limits, and behaviour; material dependencies need fallback or replacement.
RELATED CASE STUDIES
Verifiable project stage
Legal Support MCPMVP concept · The MVP scope is documented: an MCP server for rules, sources, templates, and versions, plus a thin AI-client skill. The intended output is a structured package of prioritised remarks, questions, source links, and draft wording, with material or ambiguous terms escalated to a lawyer.Marketing OptimisationCVM-function MCP MVP · The MCP MVP implements one end-to-end reactivation workflow through eight tools and has been tested on synthetic data. Its architecture targets software replacement of routine CVM work, but a real-data pilot must validate its ability to replace a full department and deliver commercial impact.
PRIMARY SOURCES
Method basis
- Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence ProfileNIST
- OWASP Top 10 for Large Language Model ApplicationsOWASP Foundation
- Artificial Intelligence Risk Management Framework (AI RMF 1.0)NIST
These sources support the method and do not validate AI SaaS Solution project outcomes.
07 · FAQ
AI agent questions
How is an agent different from a chatbot?
An agent can call tools and change workflow state, so permissions, validation, and auditability are materially stricter.
Can it access every business system?
No. Start with one workflow and minimum permissions; consequential actions remain approved or performed by a person.
How is quality measured?
Measure tool choice, arguments, task result, unsafe behaviour, abstention, latency, and cost separately.
Can you quote a universal timeline?
No. Integrations, permissions, and error cost usually matter more than the selected model.
START A PROJECT
Discuss a bounded AI agent pilot
Describe one repeatable workflow, the systems involved, and the action that currently consumes employee time.