AI AGENTS AND AUTOMATION

AI agents that handle multi step workflows so your team handles the work that requires judgment

We build autonomous and semi autonomous AI agents on LangGraph, CrewAI, and custom orchestration frameworks that reason across tools, databases, and APIs to complete multi step tasks. Every agent is tested against production edge cases, includes human in the loop escalation paths, and ships with logging and observability from day one.

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What is included

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Agent
Architecture Design

We design the agent topology for your use case: single agent for straightforward workflows, multi agent with task delegation for complex processes, or supervisor pattern where one agent coordinates and audits others. The architecture document covers reasoning strategy, tool access, memory design, and failure handling.

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Human in the Loop Escalation

We build configurable escalation paths so the agent pauses and routes to a human when confidence is low, the task exceeds its authority, or a policy rule is triggered. The human receives full context and can approve, modify, or reject the agent's proposed action.

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Tool and API Integration Layer

We connect the agent to your existing systems: CRM, ERP, ticketing platforms, databases, email, Slack, and any REST or GraphQL API. The agent can read from and write to these systems as part of its workflow execution.

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Edge Case
Testing

Before launch, we build a test suite from your real world scenarios: ambiguous inputs, missing data, conflicting instructions, API timeouts, and rate limits. The agent is tested against every documented edge case and the results are reviewed with your team.

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Memory and State Management

We implement short term (within task) and long term (across sessions) memory so the agent retains context, avoids repeating work, and builds on previous interactions. State is persisted in PostgreSQL or Redis depending on latency requirements.

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Deployment with Logging and Observability

Every agent action, tool call, decision point, and escalation is logged and visible in a monitoring dashboard. Your team can see what the agent did, why it did it, and how long each step took. Alerting is configured for failures, anomalies, and SLA breaches.

Technologies We Use

LangGraph

CrewAI

AutoGen

LangChain

FastAPI

PostgreSQL

Redis

Celery

Temporal

Grafana

Who this is for

Sales teams

Automating lead research, company profiling, outreach sequence generation, and CRM data entry that currently takes 2 to 3 hours per rep per day.

Support teams

Routing and triaging incoming tickets with AI before human handoff, auto drafting responses for common issues, and escalating complex cases with full context attached.

Operations teams

Automating invoice processing, contract review, compliance checks, data reconciliation, and report generation that currently involves copying data between systems manually.

Our Process

Discovery

Workflow mapping and agent scoping (week 1 to 2)

Prototype

Agent architecture and tool integration (week 3 to 4)

Build

Core agent build with memory and escalation (week 5 to 8)

Launch

Edge case testing and iteration (week 9 to 10)

Support

Production deployment with monitoring (week 11 to 12)

Ready to build with generative AI?

Book a free scoping call and get a tailored proposal within 48 hours.

Book a free scoping call

What is the difference between an AI agent and a simple chatbot?

A chatbot responds to a single question. An AI agent takes a goal, breaks it into steps, uses tools (APIs, databases, email), executes each step in sequence, handles errors, and delivers a completed outcome. For example, a chatbot answers 'What is our refund policy?' An agent processes an actual refund request end to end: looks up the order, checks eligibility, initiates the refund in Stripe, updates the CRM, and emails the customer.

How do you prevent the agent from making mistakes in production?

Three layers. First, the agent has explicit boundary rules (what it can and cannot do). Second, human in the loop escalation triggers when confidence is low or the action exceeds defined authority. Third, every action is logged so your team can audit, catch, and correct any issue quickly.

Can the agent work with our existing tools (Salesforce, Jira, Slack, etc.)?

Yes. We build a tool integration layer that connects the agent to any system with an API. Common integrations include Salesforce, HubSpot, Jira, Zendesk, Slack, Gmail, Google Sheets, Notion, and custom internal APIs.