# Custom AI Agent Development Build custom autonomous and multi-agent systems that plan, use tools, and report results. Production orchestration, evals, and guardrails included. Canonical: https://www.metaborong.com/services/ai/ai-agent-development Service: ai/ai-agent-development ## Overview 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. ## What is it? 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 - Deployed agent running scheduled or event-triggered jobs in production - Orchestration layer with retries, idempotency, and human-in-the-loop checkpoints - Tool-calling layer engineered against your auth and tenant boundaries - Evaluation harness with labelled multi-step task scenarios running in CI - Per-tenant cost ceilings, rate limits, and audit logging enforced at runtime - Operations runbook covering escalation, rollback, and on-call response ## How we work 1. **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. 2. **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. 3. **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. 4. **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 OpenAI (Models), Anthropic (Models), LangGraph (Orchestration), Temporal (Long-running jobs), pgvector (Retrieval), PostgreSQL (State), Redis (Queues), Sentry (Observability) ## When this fits ### Fits when - You have a multi-step workflow with clear tool boundaries and structured data behind it. - Your engineering team can absorb operational ownership of the agent after handover. - You can tolerate the latency and cost profile of a multi-step LLM workflow at scale. ### Does not fit when - You want a single-turn assistant - that is a copilot or conversational agent, not a custom AI agent. - The workflow demands sub-second latency throughout - multi-step agents are not a real-time pattern. - You expect the agent to operate without human checkpoints on high-risk steps - we will not ship that. ## FAQ ### When is a custom AI agent actually the right answer? 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. ### How do you keep agents from going off the rails? 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. ### What does production cost look like for a multi-step agent? 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. ### Will you build agents that operate without any human review? 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.