AI Agent Development

What's included

A complete agent system, not a chatbot with extra steps.

Planning, tool access, guardrails, and audit trails — the full system that makes autonomous action safe to trust.

Agent type Multi-step

Plans, calls tools, and executes multi-step tasks across your systems — not a single-turn chatbot.

Tool access Scoped

Secure connections to internal systems and third-party APIs, with per-tool permission boundaries.

Approval gates Configurable

Checkpoints where a human signs off before high-stakes or irreversible actions.

Memory Persistent

Short and long-term memory so agents stay coherent across multi-step, multi-session tasks.

Audit trail 100%

Every decision, tool call, and outcome logged for review, debugging, and compliance.

Orchestration Multi-agent

Coordinated crews of specialized agents that hand off tasks and share context.

Monitoring Real-time

Per-task latency and inference-cost tracking, so spend and speed don't quietly drift.

Not chatbots, not scripts

Agents that own a workflow.

Six shapes of agent we've designed for — each one replacing a workflow, not sitting beside it.

Support-triage agents

Classify, route, and resolve tier-1 tickets end-to-end, with human handoff the moment a request leaves the autopilot envelope.

Lead-qualification agents

Enrich, score, and schedule inbound leads directly in your CRM — before a rep has to open it.

Operations agents

Dispatch, exception-handling, and workflow orchestration that reasons over real operational data, not just trigger rules.

Research agents

Multi-source retrieval and reasoning over proprietary documents, with citations and hallucination guards built in.

Analytics agents

Conversational analytics over your data warehouse — ask a question, get a chart, and audit every step it ran.

Multi-agent systems

Coordinated agent crews that hand off tasks and share context to solve problems a single agent can't.

By business function

Agent solutions by business function.

The same agent patterns, organised by the function they support rather than the industry — useful if your team spans several.

Revenue functions

Sales OperationsPipeline-hygiene, quote-generation, and meeting-prep agents that keep the CRM accurate without a rep touching it.
Marketing OperationsCampaign-reporting, content-repurposing, and lead-scoring agents that turn raw performance data into next actions.
Customer Success & SupportEscalation-routing, renewal-risk, and onboarding agents that catch churn signals before a human would notice them.

Operations & risk

Supply ChainDemand-forecasting, procurement, and logistics-coordination agents that reason over real inventory and shipment data.
Compliance & RiskPolicy-monitoring, audit-prep, and regulatory-change-tracking agents that keep evidence current, not just on request.
Insurance OperationsClaims-triage, underwriting-support, and policy Q&A agents that read documents and flag exceptions for a human.

People & internal systems

HR & RecruitingResume-screening, interview-scheduling, and onboarding agents that free recruiters to focus on the conversation, not the calendar.
IT & HelpdeskTicket-triage, access-provisioning, and incident-summary agents that resolve the routine tier-1 volume automatically.
Finance & AccountingInvoice-processing, reconciliation, and expense-anomaly agents that handle the repetitive middle of month-end close.

Case study

Sales Ops Agent Cuts Manual CRM Work by Half

B2B SaaS · Mid-market sales technology company

Sales operations team reviewing agent workflow on screen

The challenge

Sales reps spent hours a week manually updating CRM records, drafting follow-up emails, and scheduling next steps after every call — repetitive work that ate into actual selling time.

The approach

We built a multi-step agent that listens for call-completion events, drafts CRM updates and follow-up emails for rep approval, and auto-schedules next steps — with a hard approval gate before any email actually sends.

Call completion event → agent planning loop → CRM + email draft → human approval gate → auto-scheduled follow-up

52%Less manual CRM time

100%Emails human-approved

3wkTo first agent in production

*Illustrative example based on a representative engagement.

How the engagement runs

Four steps from workflow to guarded agent.

1

Map the workflow

Identify the multi-step task, the tools involved, and where human judgement must stay in the loop.

2

Design the agent

Define the planning loop, tool permissions, memory needs, and approval gates.

3

Build & guardrail

Build the agent with scoped tool access, guardrails, and a full audit trail from day one.

4

Pilot & expand

Ship to a small pilot group, measure task success, then expand scope once trust is earned.

How we build

One framework fluency. Six disciplines agents need.

Agents that stand up fast are easy. Agents that survive contact with production need all six of these.

Agent Frameworks

Multi-step reasoning, planning, and tool-use — one framework chosen per task, not one for all.

LangGraphCrewAIAutoGenn8n

LLM Selection & Fine-Tuning

Models evaluated per task — reasoning vs. throughput vs. cost — with fine-tuning where domain accuracy earns its budget.

AnthropicOpenAIGoogleOpen-weight

Tool-Use & RAG

Agents that read your docs, query your databases, and call your APIs without prompt-engineering every new tool.

MCPFunction callingVector DBspgvector

Enterprise Integration

Auth, permissions, rate limits, and audit trails for agents operating inside real systems — not adapters.

SalesforceSAPZendeskSlack

Guardrails & Governance

Permission boundaries, human approval checkpoints, and kill-switches — agents treated as privileged users.

Approval gatesRBACPolicy layer

Observability & LLMOps

Task-level tracing, tool-call logs, and cost-per-task dashboards — instrumented like any other production service.

OpenTelemetryAudit loggingCost tracking

FAQ

Questions about AI Agent Development.

A chatbot answers questions in a single turn. An agent plans a sequence of steps, calls tools, and takes real action across your systems — with guardrails deciding what it can do without asking first.

Configurable approval gates. You decide which actions need a human sign-off (e.g. sending an email, moving money) and which the agent can complete autonomously, based on your risk tolerance.

Yes — we build scoped, permissioned connectors to your internal systems, whether that's a REST API, a database, or an internal tool with no public API at all.

A task-success evaluation suite measures completion rate, guardrail catches, and human override frequency — the same evaluation discipline we apply to every AI system we build.

It halts and escalates to a human rather than guessing. Every agent we build has a defined failure mode — stop and ask, not silently proceed.

A workflow follows a fixed sequence — input moves through predefined steps to a predictable output. An agent decides which steps to take, in what order, using which tools. Workflows are deterministic and easier to reason about; agents are adaptive and suit tasks where the right path depends on the input. For repeatable processes, a workflow is often simpler and more reliable — we'll tell you honestly which one your use case actually needs.

No, and it shouldn't try to. Agents work best as an intelligent layer that reads data via APIs and triggers actions inside the systems you already run, not as a replacement for them. The agents we build are integration multipliers — they make your existing CRM or ERP more useful, not obsolete.

Sounds like your situation?

A 30-minute call. We'll tell you honestly whether this is the right solution — and what it would take.

Explore other services

AI Strategy & Roadmapping Pilot to Production AI Fine-Tuning & Model Distillation Data Engineering & Pipelines Managed AI Operations AI Team Enablement & Training

AI Agent Development — autonomous systems that take real action, safely.