AI Agent Development
An AI agent is a system that completes a multi-step task against your existing tools — pulling data from one system, drafting a response, checking a rule, and taking or proposing an action — without a person driving each individual step. At Beacon Coders that is distinct from a chatbot, which responds to messages in a conversation; an agent does work, and the central design question for every agent we build is where human approval belongs before an action runs. Projects start at ₹3,00,000 / $3,600 and typically run 8 to 16 weeks, depending on how many systems the agent touches and how high the accuracy bar needs to be.
Where human approval belongs, and what audit trails you need
This is the honesty this page exists to give you: current models are good at drafting, summarising, retrieving and classifying, and inconsistent at judgment calls with real consequences. We scope every agent around a simple rule — the agent prepares an action, and a person confirms it before anything with a real cost attaches, until the agent has a proven track record narrow enough to trust with more autonomy. A support-triage agent that drafts a reply and flags it for review is a good early scope. An agent that issues refunds or sends contracts without review is not, unless the amounts are small and the failure mode is genuinely cheap to reverse.
Audit trails are not optional infrastructure bolted on later — every agent we build logs what data it accessed, what decision logic it applied, what it proposed, and what a human approved or rejected, timestamped and queryable. This matters for two reasons: it is how you catch a drifting agent before it causes real damage, and it is what you show a regulator, auditor or customer when something goes wrong and they ask what happened. Competitors selling agents as autonomous magic skip this section of the conversation; we lead with it, because it is the difference between an agent your compliance team will approve and one they will block after the first incident.
What's included
A decision-point map built during discovery
every step the agent will take, which ones are fully automated, and which require human sign-off
Integration with the specific tools the agent needs to act on
CRM, helpdesk, ERP, email, or internal databases — through their existing APIs
An approval workflow built into the interface your team already uses, not a separate dashboard nobody checks
An approval workflow built into the interface your team already uses, not a separate dashboard nobody checks
Audit logging for every action the agent takes or proposes, including the data it accessed and the reasoning it produced, retained and searchable
Audit logging for every action the agent takes or proposes, including the data it accessed and the reasoning it produced, retained and searchable
Guardrails that constrain what the agent can access and act on, scoped tightly at launch and widened only as real usage builds confidence
Guardrails that constrain what the agent can access and act on, scoped tightly at launch and widened only as real usage builds confidence
Testing against edge cases and adversarial inputs specifically, not just the happy path a demo would show
Testing against edge cases and adversarial inputs specifically, not just the happy path a demo would show
A monitoring dashboard so your team can see what the agent is doing in production, not just whether it is running
A monitoring dashboard so your team can see what the agent is doing in production, not just whether it is running
Tuning after launch as real usage reveals gaps the original scope did not anticipate
Tuning after launch as real usage reveals gaps the original scope did not anticipate
What's not included
AI agent development does not include training a new foundation model — we build on existing models from OpenAI, Anthropic or Google, chosen per task. It does not include fully autonomous decision-making on high-stakes actions without human review; we will not scope that regardless of what a client requests, because an unreviewed agent making costly mistakes is a liability, not a feature. It does not cover a conversational-only interface with no task execution — see AI chatbot development for that — or adding a single AI feature to an existing product, which is AI integration rather than a full agent build.
Frequently asked questions
How we scope and build an agent
Discovery focuses specifically on mapping decision points and failure modes: what should happen automatically, what needs a person, and what "wrong" costs you if it happens — this is the stage that determines the whole shape of the project, more than for any other service under AI development. Design covers the task flow and the guardrails together, not guardrails as an afterthought. Build runs in two-week sprints against a real subset of your data, not synthetic examples, since agents fail in ways synthetic data does not surface. Testing focuses on how the agent handles ambiguous or unexpected input, and launch includes the monitoring setup your team needs to trust — or catch — what it is doing.