Custom AI Agent Development
From a working idea to an agent running in production, with someone accountable for it.
- Typical timeline
- 8 to 16 weeks
- Typical timeline
- Listed below
- 7 deliverables
- Listed below
- No obligation
- Quoted after scoping
- No obligation
Built for
- Teams with a clear workflow they want an agent to carry out
- Organisations with a pilot that needs to become a production system
- Product teams adding an agent to a product their customers use
You recognise this
- You know the job you want done, and a person does it today
- A prototype impressed in a demo but is not trusted with real work
- You need the agent to act in your systems, not only to chat
The problem
Getting an agent to work once is quick. Getting it to work every day, on real data, within limits, with a person able to see and correct what it does, is most of the work, and it is where projects stall. We build for that gap from the start, through to deployment in production.
What you receive
- 01
Agent design
The workflow, the decisions the agent makes, the ones it must hand to a person, and what it is allowed to touch.
- 02
The working agent
Built against your real systems and data, with tool access limited to what the job needs.
- 03
Evaluation suite
Tests that measure the agent against your own examples and run on every change, so quality is measured rather than assumed.
- 04
Guardrails and fallbacks
Limits, approval steps for high-impact actions, and defined behaviour when a model or a tool fails.
- 05
Admin screens
Where your team reviews what the agent did, corrects it and switches it off.
- 06
Production deployment
Deployment into your environment with monitoring, logging and cost visibility.
- 07
Handover
Documentation, a runbook and a working session so your team can operate and change it.
Phases and expected timeline
- 01
Discovery and design
2 to 3 weeks
We map the workflow, collect real examples, decide where a person stays in the loop and agree what success is measured against.
- 02
Build in increments
4 to 9 weeks
Short increments against a working environment. You see it running at the end of each one, not a status report describing it.
- 03
Validation
1 to 2 weeks
Evaluation against your data, adversarial testing of the agent's limits, and behaviour under load and failure.
- 04
Deployment and handover
1 to 2 weeks
Release into production with monitoring and an evidence trail, then handover to your team.
Typical timeline: 8 to 16 weeks
Typical range for a single, well-defined scope. The clock starts once access and the people we need are in place. Your quote confirms the timeline after a scoping call. This is the range to a first production release.
Before we start
- A product owner who knows the workflow and can make decisions
- Example cases, including the hard ones and the ones that went wrong
- Access to the systems the agent must work in, in a test environment first
- Someone from your team to take over operating it
- Your security and data requirements up front, such as where data may be processed
What you can do afterwards
- A defined job is done by an agent, with a person approving the actions that need one
- You can see what the agent did and why, and switch it off if you need to
- Quality is measured against your own examples on every change
- Your team can operate and change the agent without us
What this work is designed to achieve. Results depend on your systems and your data.
Questions about this package
The one that fits the job, the data and your constraints. That can be a hosted model, an open model you run yourself, or more than one with failover between them. We explain the trade-off rather than default to one provider.
Yes. We can build alongside your team so the knowledge stays with you.
We will say so during discovery. Sometimes a rule, a script or a better form does the job more reliably, and we will recommend that instead.
Because the work depends on your systems, your data and how far the scope reaches. After a scoping call we send a written quote that states the scope, the deliverables, the timeline and the cost. Larger enterprise projects are quoted separately, based on scope, integrations, deployment requirements and ongoing support.
Yes. Say so in your enquiry and we will sign one before you share details of your systems.
Yes. For example, an architecture review can lead straight into a build, and we can quote the combined work as one engagement with one plan.
Handover includes the documentation, runbook and working session your team needs to run what we delivered. If you want us to stay involved, ongoing support is quoted separately.
Get a quote for this package
Tell us about your systems and what you need. We reply with questions or times for a scoping call, then send a written quote that sets out the scope, the deliverables, the timeline and the cost.
Larger enterprise projects are quoted separately, based on scope, integrations, deployment requirements and ongoing support.
Prefer email? info@bastionshieldtechnologies.com
Often considered alongside
AI Architecture Review
A second opinion on your AI design before you commit budget to it, with a written report and a prioritised roadmap.
Enterprise RAG Implementation
AI that answers from your own documents and data, with sources cited, permissions respected and accuracy measured.