Applied AI
Agentic AI workflows with governance
An agentic workflow is an AI system that carries out multi-step tasks by calling your systems and tools, rather than only answering questions. Done properly, every action runs under policy, with rate limits, approvals for consequential steps, and an immutable log of what the agent did and why. Hexploits built the control plane that enforces exactly this for swarmd.ai, and applies the same patterns to client workflows.
A named engineer replies within one working day. A written scope and an indicative price within two.
Who this is for
- Businesses automating processes that span several systems: onboarding, procurement, claims, reconciliations.
- IT directors who will only allow agents into production if they can see and stop them.
- AI vendors whose enterprise customers ask for audit trails before they ask for features.
What you get
Deliverables, not slogans. Each one appears in the statement of work.
- Workflow design stating which steps an agent may take alone, which need approval, and which are off-limits.
- Tool integrations with scoped credentials, rate limits and per-tenant isolation.
- A policy layer that is versioned and readable by compliance staff.
- An immutable, searchable log of every action, input and output.
- Human-in-the-loop review queues and kill switches.
Our engineers work across the major languages, frameworks and cloud platforms. We build on the stack you already run, with technology choices explained in writing before work begins.
How it is delivered
The same four stages as every Hexploits engagement, applied to this capability.
Stage 1
Map the process
We document the workflow as it runs today, the systems it touches, and where a wrong action would cost money or trust.
Stage 2
Design the guardrails first
Policy, approvals, limits and logging are designed before the agent, because they are harder to add later.
Stage 3
Build and shadow
The agent runs in a sandbox and then in shadow mode against real cases before it is allowed to act.
Stage 4
Operate
Actions, costs and exceptions are monitored; policies are updated through a reviewable change process.
How success is measured
Every engagement agrees its measures and the measurement period in writing before work starts.
- Cycle time of the process, before and after.
- Share of cases completed without human intervention, and the exception rate.
- Cost per completed case, including model and infrastructure cost.
- Zero unapproved actions, evidenced from the log.
Proof
Case studies with numbers, and reviews linked to Google where they were left there.
swarmd.ai · Software vendor · 6 months
Enterprise AI control plane delivered in six months at 75% under budget for a UK software vendor
JobVantage · Recruitment technology · Duration TBC
99.9% availability and sub-100ms responses for a recruitment intelligence platform, at negligible infrastructure cost
Director, PeppaSync
“Hexploits have been a breath of fresh air on Peppasync, an AI/ML autonomous decision platform for commercial leaders in retail and ecommerce. The depth and thoroughness the team brought to design and architecture was second to none.”
Director, JobVantage
“Working with Hexploits has genuinely been a pleasure, and I see them as my scaling partner for the foreseeable future as JobVantage grows. If you’re looking for a development team who combine strong AI/engineering capability with honesty, flexibility and a real interest in your business, I’d strongly recommend them.”
Questions we get asked
What stops an agent doing something it should not?
Which frameworks and models do you use?
Can this run on our own infrastructure?
Is an agent a high-risk AI system?
Related
Sectors where this is most often needed
Reading
ACP: The Commerce Layer for AI Agents
Customers now buy inside AI conversations. The Agentic Commerce Protocol from Stripe and OpenAI lets an AI agent run your checkout while you stay merchant of record. What it costs to adopt, who is already selling through it, and what a retailer should do now.
AP2: The Trust Layer for Agent Transactions
When an AI agent spends money on your behalf, how do you prove you authorised it? Google’s Agent Payments Protocol gives every agent transaction a signed record of intent. What it means for businesses that sell, buy or underwrite, and what to do now.
x402: The Settlement Layer for Autonomous Agents
x402 lets an AI agent pay for a web resource inside a single request, settled in seconds for a fraction of a penny. What it does to the economics of anything priced per request, its limitations stated plainly, and why for most UK businesses it is a watch item rather than a project.
More in applied ai
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AI document and email processing
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AI sovereignty: private and EU-hosted AI
AI governance and ISO 42001
AI in payments, credit and fraud under the EU AI Act
AI for recruitment: sourcing, screening and matching automation
EU AI Act for recruitment and HR: high-risk hiring AI explained
AI governance readiness for recruitment and HR businesses
Next step
Request a proposal.
Tell us about the system and the sector. A named engineer replies within one working day. A written scope and an indicative price within two working days of a short scoping call.