What separates an agent from a chatbot
An agent does not answer a question and stop. It receives an objective, decomposes it into steps, calls tools to gather what it needs, evaluates whether the result is adequate, and either finishes the job or escalates. The distinction that matters commercially is that an agent takes actions with consequences.
That is exactly why the engineering around it matters more than the model choice. Every tool an agent can call is a permission you have granted. We scope those permissions tightly, require confirmation on irreversible actions, and log every step with its reasoning so behaviour is auditable after the fact.
- Objective decomposition with explicit planning steps
- Scoped tool permissions — least privilege by default
- Human confirmation gates on irreversible actions
- Self-correction loops with bounded retries
- Complete decision logs for audit and debugging
Agent workloads that work in production
Agents earn their complexity on tasks with genuine branching — where the right next step depends on what the previous step returned. A linear pipeline does not need an agent; a deterministic workflow is cheaper, faster and easier to debug.
Where agents excel: research and synthesis across many sources, exception handling in operational processes, multi-system data reconciliation, and onboarding flows where each case follows a slightly different path.
- Research and competitive synthesis across many sources
- Operational exception handling and resolution
- Multi-system reconciliation and data repair
- Customer onboarding with case-by-case branching
- Recurring reporting that requires judgment calls
Keeping agents bounded
Unbounded agents are a production hazard — they loop, they burn tokens, and they occasionally take confident wrong actions. Every agent we ship carries a step budget, a cost ceiling, a wall-clock timeout and a defined failure mode that escalates to a human rather than guessing.
We also run agents in shadow mode before granting write access, so you can review a few weeks of decisions the agent would have made before it is allowed to make them.
What you receive
- Deployed agent with scoped tool integrations
- Permission model and escalation rules
- Shadow-mode evaluation report before go-live
- Cost, step and timeout budgets enforced in code
- Decision logs and monitoring dashboards
Technologies we use
AI Agent Development — frequently asked questions
How much does AI agent development cost?
A single-purpose agent with two or three tool integrations typically runs $18K to $40K. Multi-agent systems handling a full operational process range from $50K to $120K. Cost is driven mainly by integration count and how strict the accuracy requirement is.
How do you stop an agent doing something harmful?
Tool permissions follow least privilege, so the agent can only call what it strictly needs. Irreversible actions require human confirmation. Step budgets, cost ceilings and timeouts are enforced in code. Before write access is granted, the agent runs in shadow mode so you can audit the decisions it would have made.
Do agents need constant supervision?
No, but they need monitoring — which is different. Once evaluation shows stable accuracy, agents run unattended with alerting on drift, failure rate and cost. What they need is a periodic review of escalated cases, because those reveal where the boundaries should move.
What models do you build agents on?
Primarily Anthropic Claude and OpenAI models for planning and tool use, often with smaller, cheaper models routed in for classification sub-steps. Model choice is benchmarked against your evaluation set rather than assumed, and the architecture keeps the model swappable as capabilities change.
Can agents work with our existing systems?
Yes — that is the point. Agents call your CRM, helpdesk, database, internal APIs and document stores through scoped integrations. If a system has an API we can integrate it; where none exists we build an integration layer.
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