AI Agent Development Services We Offer in Leeds
An agent is not just an LLM with tools. Inside Leeds health and finance estates an agent that can act on a patient record or a customer account must satisfy SS1/21 model risk, DCB0129 clinical safety, Consumer Duty outcomes, and the NCSC CAF cyber assessment, all simultaneously. We build to that bar. Our agent services cover single-task agents (triage, complaint, mortgage-document extraction), multi-agent supervisor patterns for ASDA-style operations workflows, tool-using research agents for Channel 4 commissioning teams, and human-in-the-loop copilots with explicit approval gates on any state-changing tool call. Every agent ships with a documented tool inventory, per-tool scope and rate limits, an approval-required matrix for high-risk actions, full audit logging into the client's SIEM, and adversarial red-team evaluation against prompt injection, tool misuse, and jailbreak attempts.
Our AI Agent Development Development Process
Discovery opens with a regulatory routing workshop, then a tool inventory and impact assessment. Each candidate tool is classified by impact tier: read-only retrieval, advisory output to a human, reversible state change, or irreversible state change. Irreversible state changes (sending payment, prescribing medication, posting a public statement) default to human-approval-required unless the client's risk owner explicitly signs off otherwise. For NHS deployments we open DCB0129 hazard logs and engage a Clinical Safety Officer. For financial services we open SS1/21 model risk classification and Consumer Duty outcome maps. Build runs in two-week sprints with adversarial evaluation, golden-set regression on every tool call, and shadow-mode parallel operation before production. Pre-production includes a red-team engagement against prompt injection and tool misuse. Handover includes the regulatory bundle (DCB0129 file, SS1/21 validation, or CAF outcome map), the human oversight runbook, and a quarterly drift review schedule.
Process Discovery
1-2 WeeksWe sit with the people doing the work in {city} and record the real process — including the exceptions they handle by instinct, which are exactly what kill naive automations.
Tool Surface Design
1-2 WeeksEvery system the agent touches gets a typed, permission-scoped tool with its own rate limit and rollback path. The agent gets a narrow set of verbs, never raw admin access.
Build & Evaluate
3-6 WeeksThe agent is built alongside its evaluation suite from day one, using real tasks from your business with verified outcomes. Every change is scored before it ships.
Shadow Mode
2-3 WeeksThe agent runs against live traffic but commits nothing. We compare its proposed actions to what your team actually did and tune until agreement is high enough to trust.
Staged Autonomy & Run
OngoingAutonomy is released by risk band — reversible actions first, irreversible ones keeping a permanent human gate. Then we monitor completion rate, escalations, latency and spend.
Technologies We Use for AI Agent Development
Agent runtime defaults to LangGraph or Microsoft Semantic Kernel for orchestration, with Anthropic Claude 3.5 Sonnet and Claude Sonnet 4 (UK eu-west-2 via Bedrock), OpenAI GPT-4o through Azure UK South (using the NHS or commercial Microsoft tenant as appropriate), and self-hosted Llama 3.1 70B and Mistral Large on Equinix LD5 Slough or Crown Hosting Farnborough GPU clusters when SS1/21 or DCB0129 demand open weights. Tool calls run through a hardened gateway with per-tool authentication, OAuth 2.0 with PKCE for user-context tools, mTLS for service-to-service, and per-tenant rate limits. Vector stores run on pgvector in Azure Database for PostgreSQL UK South, Pinecone EU, or Weaviate self-hosted. Observability runs through Langfuse, Arize Phoenix, or Datadog LLM Observability, with full prompt, completion, and tool-call archival into the client's SIEM. Guardrails come from NeMo Guardrails, Azure AI Content Safety, and per-tool schema validation, never from a single prompt-engineering layer.
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