AI · Custom AI Agents
AI Agent Development
AI agent development is the engineering of custom autonomous agents that plan, call tools, write to your systems, and report results. These are not chatbots — they are workflows the model executes against, with checkpoints, tool layers, and human-in-the-loop wherever risk demands it. We build for the workflows that bring real operational lift and draw the line where autonomy adds risk without value. Senior engineers own the build, India + global delivery.
In short
What is AI Agent Development?
AI agent development is an engineering engagement that builds custom multi-step autonomous agents, single-agent and multi-agent, with orchestration, tool calling, evaluation harnesses, and human-in-the-loop checkpoints. Builds typically ship in eight to sixteen weeks. Senior engineers own the work end-to-end, delivered from India with global reach.
What we deliver
Concrete artefacts, not capabilities
- 01
Deployed agent running scheduled or event-triggered jobs in production
- 02
Orchestration layer with retries, idempotency, and human-in-the-loop checkpoints
- 03
Tool-calling layer engineered against your auth and tenant boundaries
- 04
Evaluation harness with labelled multi-step task scenarios running in CI
- 05
Per-tenant cost ceilings, rate limits, and audit logging enforced at runtime
- 06
Operations runbook covering escalation, rollback, and on-call response
How we work
Engagement phases
Workflow decomposition
We map the target workflow into discrete steps with explicit inputs, outputs, and failure modes. Steps that need autonomy are separated from steps that should stay deterministic - file writes, payments, identity changes. The agent's surface area shrinks to where reasoning actually helps. Everything else stays in code, not prompts.
Orchestration and tools
We build the orchestration layer - LangGraph or a custom state machine - with retries, idempotency, and explicit checkpoints. Tool calls hit your CRM, data warehouse, file systems, and product APIs through a tool layer engineered against tenant boundaries. Long-running jobs persist state and resume cleanly after failure or restart.
Evaluation and guardrails
A labelled evaluation harness covers multi-step task success, not just single-turn responses. Guardrails - input validation, output schema enforcement, tool-call rate limits, per-tenant cost ceilings - sit in the orchestration layer rather than in prompts. Regressions block deployment. Human checkpoints fire automatically wherever risk thresholds are crossed.
Rollout and operations
The agent rolls out behind feature flags and tenant cohorts. Cost, latency, and step-completion are tracked per workflow and per tenant. We hand over with a runbook covering on-call response, rollback, and escalation. Three weeks of co-maintenance close the engagement; bugs caught in production land back in the eval set.
Tech stack
What we build on
- OpenAIModels
- AnthropicModels
- LangGraphOrchestration
- TemporalLong-running jobs
- pgvectorRetrieval
- PostgreSQLState
- RedisQueues
- SentryObservability
- OpenAIModels
- AnthropicModels
- LangGraphOrchestration
- TemporalLong-running jobs
- pgvectorRetrieval
- PostgreSQLState
- RedisQueues
- SentryObservability
Scope
When this fits and when it doesn't
| This fits when | This doesn't fit when |
|---|---|
| You have a multi-step workflow with clear tool boundaries and structured data behind it. | You want a single-turn assistant - that is a copilot or conversational agent, not a custom AI agent. |
| Your engineering team can absorb operational ownership of the agent after handover. | The workflow demands sub-second latency throughout - multi-step agents are not a real-time pattern. |
| You can tolerate the latency and cost profile of a multi-step LLM workflow at scale. | You expect the agent to operate without human checkpoints on high-risk steps - we will not ship that. |
Related work
Shipped engagements
- Live project
Construction operations - agentic workflow automation
Built a multi-step agent that drafts, routes, and reconciles project documentation with human checkpoints at approval steps.
View live project - Live project
Enterprise ops - research and synthesis agent
Shipped a scheduled agent that pulls structured data, synthesises briefs, and writes back to internal systems behind tenant-level rate limits.
View live project
Related services
Adjacent engagements
Frequently asked questions
When the workflow has multiple steps, real tool boundaries, and the reasoning between steps benefits from a model. Drafting, research, multi-step ops, and structured data extraction all qualify. Single-turn classification, retrieval-grounded Q&A, and deterministic pipelines do not - those are cheaper and more reliable without an agentic layer wrapping them.
Guardrails sit in the orchestration layer - input validation, output schema enforcement, tool-call rate limits, per-tenant cost ceilings, and explicit human checkpoints. Anywhere the agent crosses a risk threshold, a human approves before the action lands. The model never writes to high-risk systems without a deterministic policy layer in between.
Higher than a copilot - multi-step agents make multiple model calls per task. We engineer per-step model routing, aggressive caching, and per-tenant cost ceilings. Cost is tracked per workflow and per tenant in production so finance gets live visibility. We project steady-state cost during the architecture phase, before the build commits.
Only for low-risk steps with bounded outcomes - research, drafting, classification. High-risk actions - payments, identity changes, irreversible writes - always sit behind a human checkpoint or a deterministic policy layer. We push back if the spec asks for autonomous agents in places where the risk profile does not justify it.
Tell us what you are building.
We build what large agencies under-deliver and freelancers can't architect, across Web3 protocols, AI agents, and SaaS products. Tell us what you are building. We will tell you how we would approach it, no pitch deck, no fluff, no commitment required.