AI agent development services — architecture that works in production.
Technical agentic AI development for CTOs and engineering leaders who need to evaluate a vendor on actual capability. We cover framework selection, agentic workflow design, multi-agent orchestration, failure recovery, and observability — the parts that determine whether an agent survives production or only works in a demo. We have four agents running in live business operations today. For a business-focused overview, see AI agents for business.
Agents scaled in 3 months — AI call quality monitoring agent
Paper approval reduction — Tejas Networks multi-agent procurement system
Production AI agents running in live enterprise operations today
Building production systems for Western enterprises since 2017
The four principles that govern every agent we build.
Modular
Each tool call independently testable. Failures are isolated, not cascading.
Auditable
Every decision logged with inputs and outputs. Behaviour is always explainable.
Recoverable
Failure triggers retry or human escalation. Silent failure is a design flaw.
Observable
Behaviour monitored after deployment. Drift is detected before it becomes a problem.
Have a specific use case? A 45-minute technical call is enough to map it to an architecture.
Book the technical callAI agent framework selection.
The AI agent framework isn't the answer — the use case defines the architecture. Here's how we evaluate each option and when we use it.
| Framework | Tool calling | Multi-agent | Memory | Best for |
|---|---|---|---|---|
| Claude Agents SDK | Native, first-class | Yes — orchestrator/subagent | Session + external store | Complex reasoning, auditability, enterprise |
| OpenAI Agents SDK | Native, first-class | Yes — handoff model | Session-based | OpenAI ecosystem, fast prototyping |
| LangGraph | Via tools | Yes — graph state machine | Configurable | Complex stateful workflows with branching |
| Custom Python | Direct API | As designed | As designed | Maximum control, simple use cases |
We use Claude SDK for enterprise engagements where auditability and complex reasoning matter. OpenAI SDK when the client is already in the OpenAI ecosystem. LangGraph for complex stateful agentic workflows. Custom Python when frameworks add overhead without benefit. We'll tell you which fits your use case after a 45-minute technical call.
What an agentic workflow looks like in production.
An agentic workflow is a multi-step process where an AI agent reads data from one system, makes a decision, takes an action in another, and continues based on the outcome — without a human step between each stage. Single agents handle well-defined tasks with a clear input-output loop. Multi-agent systems handle tasks with parallel workstreams or verification steps that need an independent actor.
For large organisations with compliance, data residency, and deep ERP integration requirements, see our enterprise AI agents service.
Example: procurement approval agentic workflow
Receives purchase request, coordinates the workflow, makes final approval decision
Checks vendor certifications, approval limits, and policy rules
Validates against department budget and outstanding purchase orders
Creates PO in ERP, updates inventory projections, notifies finance
Four agents in production — and what made them work.
The architecture decisions that determined whether each agent worked in production. Not demos, not PoCs. The same principles apply to every AI agent development engagement we take on.
Call Quality Monitoring Agent
Event-driven pipeline that ingests call recordings, runs them through a custom scoring model, and produces structured quality reports — no human listener at each step. The architectural challenge: consistent scoring at volume with low latency, using a fine-tuned classification model trained on client-specific quality criteria.
agents scaled in 3 months without adding QA headcount
Procurement Workflow Automation Agent
Multi-agent system: an orchestrator agent coordinates three specialised sub-agents handling compliance checking, budget validation, and ERP updating. Full decision audit trail at every step. Integrated with Tejas Networks' existing procurement infrastructure — compliance rules and approval limits enforced programmatically.
reduction in paper-based approvals at Tejas Networks
Manufacturing Cost Estimation Engine
ML regression model trained on historical job cost data, deployed as a real-time inference API. Input: product specifications. Output: itemised cost estimate in under 2 seconds. The hard part was feature engineering from irregular historical records and continuous retraining as material costs change — not model selection.
quote turnaround — manual spreadsheet replaced by real-time ML output
CRM Lead Scoring Agent
Reads deal signals from CRM records, enriches with external company data, and scores against a model trained on historical win/loss patterns. Runs on a refresh cycle and updates in real time when new activity is logged. Data quality — not model selection — was the architectural bottleneck.
pipeline qualification running in production for a B2B sales team
Production agentic AI. Not a proof-of-concept.
We have AI agents running in production for enterprise clients. A call quality monitoring agent scaled a contact centre from 50 to 80+ agents in 3 months. A procurement agent cut paper-based approvals at a publicly listed company by 90% — see the Tejas Networks case study. A cost estimation agent replaced a 3-day manual process with real-time ML output.
These are the reference points for what we scope. If your use case is similar in complexity, we know what it takes. If it's more complex, we'll tell you before you spend on a build.
Book a technical review callTechnical review call
45 minutes. You describe the use case. We map it to an architecture — framework, tool design, data access, failure modes. You get a straight assessment of what's viable and what the build looks like.
Technical questions about agentic AI development.
Still have questions?
Talk to us directly — no forms, no waiting for a sales rep.
Have an agent architecture to validate?
Describe the use case. We'll map it to a framework, define the tool design, and tell you what it would take to ship in production. 45 minutes. No pitch.
Book a technical call