Design and deploy autonomous AI agents that reason, plan, and execute multi-step workflows across tools and APIs. Kansoft builds production-ready agentic systems for global enterprises — from single-domain bots to multi-agent orchestration frameworks.
Business processes require multi-step reasoning, tool use, and real-world action — capabilities that retrieval chatbots simply can't provide.
Research, summarisation, drafting, and decision support tasks consume hours of skilled-worker time every day.
Processes that span CRM, ERP, calendar, email, and databases require an agent, not a single prompt.
Existing chatbots can answer questions but can't take action, fetch live data, or execute a multi-step plan.
RPA bots handle structured tasks but break on unstructured inputs — exactly where AI agents excel.
Ad-hoc LangChain scripts deployed to production fail silently, lack observability, and can't be audited.
Approval and escalation bottlenecks nullify automation gains. Agents with configurable autonomy solve this.
We design agentic systems with production constraints in mind from day one: latency budgets, tool reliability, escalation paths, audit trails, and cost controls. The result is an agent you can actually trust in production.
Define agent topology (single vs. multi-agent), tool registry, memory strategy, and autonomy levels for each workflow step.
Build secure, rate-limited tool wrappers for APIs, databases, browsers, code interpreters, and internal systems.
Select and tune the LLM backbone, implement ReAct/CoT prompting, and configure task decomposition and replanning.
Instrument every agent step with tracing, cost metering, output validation, and configurable human-in-the-loop checkpoints.
Agents that search the web, internal knowledge bases, and documents to produce structured research reports autonomously.
AI coders that write, test, review, and refactor code within guardrails — integrated into your CI/CD pipeline.
Agents that triage inboxes, draft responses, schedule meetings, and handle follow-ups across enterprise mail systems.
Multi-agent systems that decompose complex business processes into parallel subtasks executed by specialised sub-agents.
Natural-language-to-SQL agents that query databases, generate charts, and produce narrative summaries on demand.
Agents that continuously monitor systems, flag policy violations, and generate compliance evidence automatically.
From discovery to production in six weeks — with a structured checkpointing process at each stage.
Map the target workflow, identify tools and systems the agent needs to access, define success criteria and safety boundaries.
Design agent topology, build tool wrappers, select LLM backbone, and establish the memory and state management strategy.
Implement reasoning loop, task decomposition, tool invocation, and initial guardrails. Deploy to staging with synthetic tasks.
Red-team agent behaviour, add fallback paths, tune prompts for edge cases, instrument tracing and cost metering.
Production release with runbook, monitoring dashboards, escalation playbook, and team training on agent operations.
A consulting firm deployed a LangGraph agent that reads contracts, flags non-standard clauses, suggests redlines, and routes to the right legal reviewer. Review time dropped from 4 hours to 45 minutes per contract.
A digital lender built a multi-agent system that collects documents, validates identity, queries credit bureaus, and produces a risk-scored recommendation — with full audit trail for regulatory compliance.
A SaaS company deployed a code-review agent that analyses pull requests, identifies bug patterns, checks test coverage, and posts structured review comments. Engineers close PRs 40% faster.
Every agent ships with output validation, fallback paths, and configurable autonomy caps. We never hand over an agent that can take irreversible actions without a checkpoint.
LangSmith or Langfuse tracing on every agent run — cost per task, latency, tool failure rates, and prompt versions all tracked.
We choose the right framework for your stack (LangChain, AutoGen, custom) rather than defaulting to one tool for every problem.
Tool permissions follow least-privilege, secrets are never in prompts, and all external calls are logged and rate-limited.
All agent code, prompt libraries, evaluation suites, and runbooks are yours at handover — no ongoing vendor lock-in.
Teams across India, UAE, USA, Europe, and Australia — same-day responses and workday overlap regardless of your timezone.
A chatbot answers questions in a conversation window. An AI agent takes actions: it calls APIs, queries databases, writes files, sends emails, and executes multi-step plans. Agents use an LLM as a reasoning engine but connect it to tools that interact with the real world.
We implement configurable autonomy levels — agents require human approval for high-stakes or irreversible actions. All tool calls are logged, rate-limited, and validated against an allowlist. Output guardrails check responses before any action is committed.
We select the LLM based on your requirements — latency, cost, context window, and data residency. We commonly use Claude (Anthropic), GPT-4o (OpenAI), or Gemini, and can use open-source models (Llama 3, Mistral) for on-premise deployments.
Yes. We build tool wrappers for Salesforce, SAP, ServiceNow, Microsoft 365, Jira, Confluence, and any system with an API. For legacy systems without APIs, we can integrate via browser automation with appropriate controls.
Data minimisation is a core design principle — agents only access the data needed for the specific task. We implement role-based tool permissions, redact PII before LLM calls where possible, and can deploy agents entirely on-premise or in your private cloud.
