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AI Agents.

Autonomous and human-in-the-loop agent systems with real tool use, persistent memory and rigorous guardrails. Evaluation harness, observability and replay tooling on every engagement.

Overview

What “AI Agents” means at Devmint.

AI Agent Development at Devmint means building autonomous and semi-autonomous systems that reason, plan, call tools and complete real work — not chatbots dressed up as agents. We design the orchestration layer, the tool interfaces, the memory model, the failure recovery and the human-in-the-loop checkpoints that turn an interesting prototype into a process your operations team actually trusts on a Monday morning.

Most agent systems we audit fail in the same three places: brittle tool definitions, no recovery path when a step fails, and zero observability into why a decision was made. Devmint engagements start from the assumption that agents will fail, models will drift, and the only durable system is one where every action is logged, every decision is replayable, and a human can take over without losing context.

What you get

Deliverables.

  • Orchestration + tool-use architecture
  • Memory + retrieval layer
  • Eval harness and replay tooling
  • Human-in-the-loop checkpoints
  • Observability dashboard + runbook
How it ships

The shape.

Agent engagements run 8–14 weeks. Week one writes the eval and the failure-mode map; weeks two onward ship one capability at a time, each in front of a human reviewer with full audit logging.

Investment

How we price.

Devmint engagements are scoped as fixed proposals against measurable outcomes — not hours. After a 30-minute discovery call, we send a written proposal with timeline, deliverables, eval targets and a single fixed fee. No procurement maze, no T&M creep. Smaller pilots and larger outcome-based contracts are scoped the same way — tell us what you're shipping and we'll come back with a number.

Tech stack

What we reach for.

LangGraph or custom orchestration · Anthropic and OpenAI models · pgvector + BM25 hybrid retrieval · Langfuse for traces · Postgres for audit and replay · Inngest for jobs · Next.js + TypeScript for the human-review surface.

FAQ

Common questions.

We constrain the action surface with typed tools, validate every tool input and output, log every decision, and require a human checkpoint before any high-stakes action. Plus an eval harness that runs on every PR.
Yes. Devmint's integration model is to operate inside your codebase, your auth and your observability — we ship the agent layer as a feature in your system, not a separate vendor surface.
A single-purpose agent in production typically takes 3–6 weeks. Multi-agent workflows and platforms with custom tool ecosystems run 8–14 weeks.
Next step

Have a ai agents project on your roadmap?

Send us a one-paragraph problem statement and we'll come back with a sample architecture and rough timeline within 48 hours.