AI Agents
Part of AI & Automation
In short
A custom AI agent is a software system that uses a language model to decide which actions to take, then executes them through defined tools and APIs. Offsage builds agents for specific business operations — research, qualification, document handling, internal support — with evaluation, permission scoping and human review. Custom agent builds start at $5,000.
Key takeaways
- An agent is defined by its tools and permissions, not by the model behind it — most reliability problems are scoping problems.
- Every build ships with an evaluation set, so quality regressions are detected rather than discovered by a customer.
- Guardrails and escalation paths are designed in from the start, not added after an incident.
- Where a deterministic workflow solves the problem, Offsage builds that instead — an agent is not always the right tool.
By Sai Abhipsa Dash, Co-Founder & CEO. Last reviewed 8 October 2026.
How this works
The useful distinction is between a workflow and an agent. A workflow follows a defined sequence; an agent decides what to do next. Agents earn their cost where the path genuinely varies — triaging an inbound request, researching a prospect across sources, resolving a support question against documentation. Where the path is fixed, a deterministic integration is cheaper, faster and more reliable, and we will say so.
Reliability comes from constraining the action space. Offsage scopes each agent to a specific set of tools with the minimum permissions required, defines what it may and may not do, and specifies explicit escalation conditions. An agent that cannot take a destructive action cannot take one by mistake, which matters more than any prompt engineering.
Every deployment ships with an evaluation set built from real cases, so accuracy is measured continuously rather than assumed. Combined with logging of decisions, escalations and failures, that makes it possible to answer the only question that matters operationally: is this thing still doing its job?
What this covers
- Agent design: task scoping, tool definition and permission boundaries
- Tool and API integration with least-privilege access
- Retrieval over internal documentation and knowledge bases
- Evaluation sets built from real cases, run continuously
- Guardrails, refusal conditions and human escalation paths
- Memory and state management appropriate to the task
- Observability: decision logging, escalation tracking, failure analysis
- Cost and latency monitoring against model selection
Typical stack
- OpenAI
- Anthropic
- OpenClaw
- PostgreSQL
- Next.js
- n8n
Indicative pricing
Directional starting points, not ceilings. Final scope is agreed after a free 30-minute strategy call.
Agent prototype
A scoped proof of concept on one task, with a fixed tool set and an evaluation set, to establish whether an agent is the right solution before committing to a build.
From $1,500
Custom agent build
A production agent with tooling, permissions, evaluation, guardrails, escalation and observability, deployed into your environment and monitored.
From $5,000
See all pricing ranges across every service, including retainers and terms.
Questions on ai agents
Common production tasks include triaging and routing inbound requests, researching and enriching prospect or account data, answering questions against internal documentation, extracting structured data from documents, and assembling recurring reports. The consistent pattern is a task with variable steps, a defined set of actions, and a clear escalation path for cases the agent should not handle.
It depends on the task. Offsage tests against the current frontier models from OpenAI and Anthropic and selects on measured accuracy, latency and cost for the specific job rather than on brand. Where an open-source runtime such as OpenClaw suits the requirement, that is used instead of a bespoke build.
By constraining what it can do and measuring whether it does the job. Each agent receives the minimum tool permissions required, explicit refusal conditions, and an escalation path for anything outside scope. Every decision is logged, and an evaluation set runs continuously so degradation is caught before it reaches a customer.
Often not. If the process follows a predictable sequence, a deterministic workflow is cheaper, faster and more reliable. Agents are worth building where the path genuinely varies and judgement is required. Offsage will recommend the simpler option when it solves the problem.
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