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
Agent projects start at ₹3,00,000 / $3,600 and typically run 8 to 16 weeks. What moves the price: how many systems the agent integrates with, how tight the accuracy requirement is, and how much of the workflow requires human review versus full automation. Full engagement models are on the pricing page.
It belongs anywhere an incorrect action has a real cost that is hard to reverse — sending an external communication, issuing a refund, changing a customer's account status, or committing to a contractual term. It is less necessary for internal, low-stakes, easily reversible actions like drafting an internal summary or tagging a record for someone to review later. We decide this per action during discovery based on what happens if the agent gets it wrong, not a blanket rule.
At minimum: what data the agent accessed, what it proposed or did, who approved it (if anyone), and when — all timestamped and retained for as long as your compliance or dispute-resolution needs require. For regulated industries this extends to logging the model version and prompt used, so a decision from six months ago can be reconstructed if questioned. We size the audit trail to your industry's actual requirements during discovery.
Most of our agent projects are scoped to handle the repetitive first pass of a task — sorting, drafting, checking — and hand judgment calls to a person, which frees up hours rather than removing a role outright. Whether an agent can go further than that depends on how much of the role is genuinely routine versus how much requires judgment a model cannot yet reliably replicate; we assess this honestly during discovery rather than overselling the answer.
Through scoped access — the agent can only reach the systems and data explicitly granted during setup — combined with approval gates on any action above a defined risk threshold and logging on everything it does. No agent is error-free; the design goal is making errors visible, reviewable and recoverable rather than claiming they will not happen.
That depends entirely on real production performance, not a fixed timeline. We start narrow, monitor the audit trail and approval outcomes closely, and only widen the agent's autonomy on specific action types once the data shows a consistent, low error rate on those actions — a decision we make with you based on evidence, not on a vendor's confidence.
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.