Agents That Complete Work,
Not Just Conversations
An agent differs from a chatbot in one important way: it acts. Given an objective, it plans a sequence of steps, calls the tools it needs, checks its own progress and reports back. That capability is genuinely useful — and genuinely requires careful bounds, which is where most of our engineering goes.
Our AI Agent Development Services
Designing, building and operating agents that are trusted with real tasks.
Agent Architecture
We define the objective, decompose it into steps an agent can reliably execute, and decide where autonomy genuinely helps versus where a deterministic workflow would be safer and cheaper.
Tool & API Design
An agent is only as capable as the tools it can call. We build well-scoped, well-documented tools with strict permissions, so the agent can act usefully without ever exceeding its remit.
Multi-Agent Systems
For complex objectives, specialised agents coordinated by an orchestrator — each with a narrow remit, which is markedly more reliable than one agent attempting to do everything.
Memory & Context
Short-term working memory for the task in hand and durable memory across sessions, so an agent accumulates useful context instead of restarting from nothing each time.
Guardrails & Oversight
Approval gates on consequential actions, spending and rate limits, sandboxed execution, and a complete audit trail of every decision, tool call and result.
Evaluation & Operations
Task-level success measurement, cost and latency telemetry, failure replay, and the monitoring needed to run agents in production with confidence.
What Our Agents Can Do
The core competencies we engineer and then verify against real tasks.
Planning & Decomposition
Break an objective into ordered steps, adapt the plan when a step returns something unexpected, and recognise when the goal has been met.
Tool Use
Call APIs, query databases, read and write documents, trigger workflows, and interpret what came back well enough to decide the next step.
Self-Correction
Detect a failed or implausible result, diagnose the likely cause, and retry with a different approach rather than propagating the error downstream.
Bounded Autonomy
Operate freely inside an explicitly defined envelope of permissions, budget and action types, and stop for human approval at every boundary.
Where Agents Earn Their Keep
Multi-step work that is too variable to script and too repetitive to justify a specialist's full attention.
Research & Synthesis
Gather information across internal and external sources, cross-check it, and produce a cited briefing in a fraction of the time a manual review would take.
Document Workflows
Review a contract or submission against a checklist, extract obligations, flag deviations from standard terms and draft the summary for a specialist to approve.
Reporting & Analysis
Pull data from several systems, reconcile it, produce the recurring analysis and highlight what changed and why it matters.
Complex Case Resolution
Work a support case that spans multiple systems — verify entitlement, check history, apply the remedy, confirm the outcome — end to end.
Engineering Assistance
Triage issues, reproduce bugs, propose fixes and open pull requests against your conventions, with review firmly retained by your engineers.
Operations Coordination
Monitor for conditions that require action, gather the relevant context, and either execute the routine response or escalate with everything already assembled.
Why Our Agents Are Trusted in Production
Autonomy Is Earned Gradually
New agents start in recommend-only mode where a person approves each action. Autonomy expands scope by scope as measured reliability justifies it — never by default on day one.
Every Action Is Auditable
Full traces of reasoning, tool calls, inputs and outputs. When something goes wrong you can see precisely where, which is what makes agents defensible to risk and compliance teams.
Bounded Cost and Blast Radius
Hard limits on spend, iterations and permissions mean a misbehaving agent stops safely instead of looping expensively or reaching something it should never have touched.
Our Agent Development Stack
Models
- Claude
- GPT
- Gemini
- Open-weight models
- Tool-use APIs
- Structured outputs
Frameworks
- Claude Agent SDK
- LangGraph
- Model Context Protocol
- Python
- TypeScript
- FastAPI
Memory & State
- Postgres / pgvector
- Redis
- Temporal
- Object storage
- Event sourcing
- Trace stores
Platform
- AWS
- Azure
- Google Cloud
- Docker
- Kubernetes
- Sandboxed runtimes
AI Agent Development — Common Questions
A chatbot responds; an agent acts. Given a goal, an agent plans steps, calls tools, evaluates what it gets back and iterates until the objective is met or it hits a limit. A chatbot tells a customer how to reset a subscription — an agent verifies the account, applies the change, confirms it and records the outcome.
Layered constraints. Tools are scoped so the agent can only perform explicitly granted actions; consequential steps require human approval; spend, iteration and rate limits cap the blast radius; execution is sandboxed; and everything is logged. New agents also begin in recommend-only mode so reliability is demonstrated before autonomy is widened.
For well-scoped tasks with good tools and clear success criteria, yes — and that qualification matters. Reliability falls sharply as scope broadens and the number of steps grows, which is why we favour several narrow agents over one general one, and why we measure task-level success rates continuously rather than assuming they hold.
Agents consume more tokens than a single-turn assistant because they iterate, so cost control is a design concern rather than an afterthought. We route simpler steps to smaller models, cache aggressively, cap iterations and instrument per-task cost, so you can see the unit economics and set limits deliberately.
That is generally the point of building custom ones. We expose your systems as governed tools with authentication and permission scoping, so an agent operates within the same access boundaries a staff member in that role would have — and no wider.
A single, well-defined, repetitive multi-step task with a clear definition of done and low cost of error. It establishes the tooling, evaluation and oversight patterns while the risk is small, and gives you real reliability data before you extend agents into more consequential work.
Other AI Solutions We Build
Generative AI
Secure LLM systems for decision support, drafting and knowledge retrieval that augment human judgement.
Generative AI Consulting
Strategic roadmaps that take AI from boardroom ambition to measurable production workflows.
AI Chatbot
Context-aware conversational interfaces that resolve customer questions around the clock.
AI Automation
Intelligent automation that removes repetitive work so your team can scale without scaling headcount.
Put an agent on the work that's piling up
Tell us about a repetitive, multi-step process. We will assess whether an agent is the right approach and how we would bound it safely.