AI Agent Development Services

Processes best suited for AI agents are information-intensive, follow deterministic decision paths in most cases, involve multiple data sources, and have clear success criteria — research, triage, data processing, report generation, and coordination workflows.

We design bounded action spaces, output validation layers, confidence thresholds, human approval checkpoints for high-stakes actions, and comprehensive error recovery. Agent systems are extensively evaluated before deployment and monitored continuously in production.

Focused single-agent deployments start from $60,000. Multi-agent enterprise systems with complex integrations range from $200,000 to $800,000. We provide detailed investment cases with projected ROI during scoping.

Focused agents with well-defined scope and existing integrations deploy in 6–10 weeks. Complex multi-agent systems with new enterprise integrations typically require 14–20 weeks.

Yes. We build conversational interfaces connecting agents to Slack, Microsoft Teams, web applications, and internal portals — with session management, authentication, access controls, and usage analytics.

Cost efficiency is designed into the architecture — using appropriate model tiers per task, implementing caching for repeated queries, optimising prompt lengths, and monitoring per-task token consumption against defined cost budgets.

Yes, with appropriate design. We implement compliance-compatible human oversight workflows, full audit trails, role-based access controls, and output review queues — enabling agent deployment in regulated environments.

Our agent architectures include structured error recovery, fallback behaviours, confidence-based routing, and human escalation pathways. Agents are designed to fail gracefully — surfacing uncertainty rather than proceeding with low-confidence actions.

Yes. We implement RAG architectures giving agents access to your internal documentation, policies, and historical data via vector search — grounding responses in your proprietary knowledge.

Production agents require monitoring for task success rate, latency, cost per task, and error patterns. We provide managed monitoring and structured optimisation sprints to improve performance over time.

We build on OpenAI and Anthropic Claude models for reasoning, orchestrated through LangChain and similar agent frameworks — selecting the model tier per task rather than defaulting to the largest available model.

If the workflow and success criteria are already clear, we build directly. If you're unsure which process is actually a good fit for agent automation, our AI Consulting engagement scopes that first — most agent projects that fail were never a good fit for autonomy in the first place.

Agents operate under the same access-control and audit-logging discipline as any production system with credentials — scoped permissions per tool, full action logging, and no agent is given broader system access than the task strictly requires.

Both. Startup engagements are typically a single well-defined agent solving one workflow; enterprise engagements involve multiple coordinated agents with governance and audit requirements layered in — same engineering discipline, different scope.

We sign mutual NDA before discussing your specific workflows or data. Scoping then focuses on whether the task is genuinely agent-shaped (bounded, verifiable, tool-accessible) before any build commitment is made.