SM> saswatbuilds
> AI AGENT DEVELOPMENT

Custom AI agents that take real work off your team's plate

I design and build autonomous LangGraph/LangChain agents and multi-agent systems that research, decide, and act across your workflows — with human-in-the-loop control, evals, and observability so they hold up in production.

Book my free 30-min AI scoping callSee case studies
Free · 30 min · no obligation · reply within 1 business day
80%
less manual research time on a shipped lead-gen agent
12
coordinated agents in one production GTM system
124
tests on a single delivered multi-agent build
From $2,500 · typical projects $7,500–$30,000 · billed at $60/hr or $2,500/weekSee pricing & packages →

Custom AI agent development is building autonomous LangGraph and multi-agent systems that reason, call your tools, and finish multi-step work — not chatbots. It is for B2B SaaS and services teams drowning in manual ops, research, or outreach. The result: reliable agents your team trusts to take real work off their plate, shipped to production with guardrails and evals.

> The problem & the outcome

Most "AI agents" never survive contact with production

Demos are easy; reliable agents are hard. The gap is everything that happens after the happy path — tool errors, hallucinated arguments, runaway loops, silent failures, and no way to see what the agent actually did. Teams end up babysitting a system that was supposed to save them time.

I build agents the way you build the rest of your stack: explicit state machines, typed tool calls, retries and guardrails, human-in-the-loop checkpoints on high-stakes actions, and tracing so every decision is auditable. The result is an agent your team trusts enough to actually hand work to.

> What you get

Scope & deliverables — everything needed to ship it reliably

Agent architecture & scoping

We map the workflow, decide where an agent helps vs. plain automation, and define success metrics before writing code.

LangGraph state machines

Deterministic graphs with explicit nodes, edges, and state — not a prompt-and-pray loop. Easy to reason about and extend.

Typed tools & integrations

Pydantic-validated tool calls into your APIs, databases, CRMs, and third-party services, with retries and error handling.

Human-in-the-loop control

Review/approve gates on irreversible or high-stakes actions, plus override and edit paths.

Evals & observability

Test suites over real cases, tracing (LangSmith/OpenTelemetry), and dashboards so you can see and trust agent behavior.

Handover & docs

Clean repo, runbook, and a walkthrough so your team can operate and extend the system.

> How I work

A low-risk path from idea to production

1 · Scoping call

Free 30 minutes to pressure-test the use case, ROI, and the riskiest unknowns.

2 · Prototype

A working vertical slice on your real data within 1–2 weeks to de-risk the approach.

3 · Build & harden

Full implementation with tools, guardrails, evals, and observability.

4 · Ship & support

Deploy, monitor, and iterate against real usage; optional retainer for ongoing work.

> Stack

The stack I build on — chosen for your use case

LangGraphLangChainGPT-4oClaudePydantic v2PythonLangSmithFastAPIStreamlit
> Proof

Proof: shipped systems and the numbers they moved

CCAI AGENT DEVELOPMENT · LIVE
Claude Cowork — LinkedIn Multi-Agent GTM System

12 autonomous agents running an entire LinkedIn growth + outbound motion

+5,735% 28-day impressions lift (to 3,676)
Built by Saswat Mishra · AI engineer — architecture, build, deploy
Read the case study →
ABAI AGENT DEVELOPMENT · DELIVERED
AI B2B Lead Engine — LangGraph Multi-Agent Sales System

A 6-agent hub-and-spoke StateGraph that qualifies, scores, and works B2B leads across LinkedIn, email, and voice

6 coordinated agents in one StateGraph
Built by Saswat Mishra · AI engineer — architecture, build, deploy
Read the case study →
> FAQ

AI Agent Development: questions buyers ask

?What is custom AI agent development?

It is building an autonomous system that uses an LLM to reason over a goal, choose actions, call tools/APIs, and complete multi-step tasks — tailored to your workflow rather than a generic chatbot. In practice that means a state machine (often LangGraph), typed tools into your systems, guardrails, and human checkpoints.

?LangGraph or CrewAI or AutoGen — which do you use?

I default to LangGraph for production work because its explicit state graph makes agents debuggable and reliable, but the right choice depends on the task. I cover the trade-offs in detail in my LangGraph vs CrewAI vs AutoGen comparison.

?How much does a custom AI agent cost?

I bill at a flat $60/hour or $2,500/week, so cost tracks weeks of work: a focused single-agent build (about 1–3 weeks) typically runs $2,500–$7,500, and a complex multi-agent system (6–12 weeks) is $15,000–$30,000. The biggest driver is integration surface and reliability requirements. See my full AI agent cost guide for the breakdown.

?How do you keep agents from hallucinating or going off the rails?

Typed tool calls (the model can only act through validated functions), explicit graph control flow, retries with bounded loops, output validation, human-in-the-loop gates on risky actions, and eval suites that run on every change.

?Can you work with my existing stack and data?

Yes. Agents integrate with your APIs, databases, CRMs, and SaaS tools. I handle auth, rate limits, and data-residency constraints, and I work across US/UK/UAE/Singapore time zones.

> GO DEEPER
ARTICLE
How Much Does It Cost to Build a Custom AI Agent or Automation in 2026?
What a custom AI agent costs in 2026 — from $1,500–$5,000 for single-task bots up to $150,000+ for multi-agent systems, plus run costs and ROI by scope.
ARTICLE
LangGraph vs CrewAI vs AutoGen: Which Wins for Production? (2026)
LangGraph vs CrewAI vs AutoGen for production AI agents in 2026: LangGraph for reliability, CrewAI for fast prototypes, AutoGen now in maintenance mode.
ARTICLE
How to Build a Production-Grade AI Agent with LangGraph (2026 Architecture Guide)
A production architecture for AI agents with LangGraph: state graphs, the plan–act–verify loop, tool calling, human-in-the-loop, guardrails, and evals.
ARTICLE
Why AI Agents Fail in Production (And How to Prevent Each Failure)
Why AI agents break in production — no guardrails, no evals, no observability, runaway loops, and bad tool design — plus a practical fix for each.

Let's see if I can take this off your plate

Tell me what you want to automate. On a free 30-minute call I’ll tell you straight whether it’s worth building, roughly what it costs, and how I’d approach it — no pitch, no obligation.

Book my free 30-min AI scoping call
Free · 30 min · no obligation · reply within 1 business day