AI agent development is the engineering of autonomous software that uses large language models to plan and complete multi-step tasks — file tickets, update CRMs, run research, execute workflows — with human-in-the-loop guardrails on impactful actions. iMagic Solutions is an AI agent development company building production-grade agents on AWS Bedrock AgentCore (preferred for observability and tracing), LangGraph and CrewAI. We serve clients across the USA, Europe and India with senior AI engineers, evaluation harnesses on every build, and a fixed-price proof-of-concept before full engagement.
AI agents are the 2026 inflection point past chatbots. A chatbot answers a question; an AI agent takes an action — books the meeting, files the JIRA ticket, updates the Salesforce opportunity, runs the multi-step research and posts the brief in Slack. That ability to act, not just generate text, is what separates production agentic AI from the year of impressive-but-useless demos that preceded it. The companies winning with AI in 2026 are deploying agents in narrow, high-volume workflows — qualified-lead routing, support deflection with action-taking, internal-tool automation, document processing — and measuring real headcount displaced.
We build AI agents across five complexity tiers: Tier A single-action read-only ($15K–$30K), Tier B single-action write-back ($25K–$50K), Tier C multi-action workflow ($40K–$90K), Tier D multi-system autonomous ($80K–$180K), Tier E enterprise autonomous with 5+ system integrations, role-based access control and SOC 2 / HIPAA / GDPR compliance ($150K–$300K+). The defining cost drivers are: how many actions the agent can take, whether actions are read-only or change state, the number of system integrations, and whether the agent runs autonomously or with human checkpoints on impactful actions.
AWS Bedrock AgentCore is our default platform for production agents because it ships managed observability, action-tracing, evaluation hooks and works inside your AWS account on Bedrock-eligible models. AgentCore handles the agent-orchestration plumbing (tool definitions, action invocation, retry logic, state) so engineering time goes into business logic, not framework. For projects that need fine-grained graph-based orchestration or multi-agent supervision patterns AgentCore can't express cleanly, we use LangGraph (graph-based) or CrewAI (multi-agent role-playing). We benchmark on the specific task before committing to a framework.
Every agent we ship has three defenses against doing damage: tool-permission scoping (the agent literally cannot call APIs it doesn't have an IAM grant for), human-in-the-loop checkpoints on impactful actions (the agent drafts the refund/email/contract change but a human approves before execution), and an evaluation harness that tests the agent against a held-out test set of 100–500 scenarios before any prompt or model change ships to production. Without these, autonomous agents are a liability; with them, they're a force multiplier that displaces 5–15 FTE worth of routine knowledge work per deployment.
We work model-agnostic: Claude Sonnet via AWS Bedrock for default reasoning, GPT-4o or GPT-5 for the hardest planning tasks, Claude Haiku or Amazon Nova for cheap fast subtask handling, and Llama 3.3 self-hosted when full data control is required. Most production agents we build use multiple models in one stack — a planner model decides what to do, a worker model executes individual subtasks — which delivers 40–60% cost savings without quality loss. Every engagement starts with a free 30-minute discovery call and a fixed-price 2–4 week proof-of-concept on real data before committing to a full build.
Effectively a chatbot with one tool — lookup, search, summarise, retrieve. No write-back, no multi-step planning. 3–5 weeks. Common first agent project for teams new to agentic AI.
One tool that takes an action — file a Zendesk ticket, send a Slack message, update a Salesforce opportunity. Adds permission scoping, audit logging, idempotency and human-in-the-loop checkpoints. 5–7 weeks.
Agent orchestrates 3–8 actions to complete a workflow — qualify lead, enrich, score, book meeting, follow up, update CRM. Adds state management, retry logic, partial-failure handling and richer evaluation. 7–10 weeks.
Semi-autonomous agent that takes a goal, breaks it into steps, calls multiple tools, evaluates results and decides next steps. AWS Bedrock AgentCore for observability, evaluation against held-out scenarios. 10–14 weeks.
Multiple agent personalities coordinated by a supervisor agent, 5+ system integrations, RBAC inherited from identity provider, audit logging, deployment inside your own AWS account, SOC 2 / HIPAA / GDPR compliance. 12–20 weeks.
2–4 week fixed-scope PoC on real data with one real integration and the actual model. Output: a working agent, accuracy and action-correctness report, cost projection. Credited toward full build if you proceed.
Automated evaluation against held-out test scenarios, action-correctness scoring, prompt-version control, human-in-the-loop integration, audit logging — added to existing agents that shipped without proper guardrails.
Supervisor-and-specialist agent architectures (CrewAI, LangGraph) for complex workflows where one agent isn't enough — research-and-write systems, customer-success agent fleets, compliance pipelines.
Add Langfuse / Helicone tracing, cost dashboards, planner-worker routing and prompt caching to existing agents. Typical 40–60% LLM bill reduction without quality loss.
Monthly retainer covering accuracy tuning, prompt iteration, new-tool integration, LLM cost optimization and human-in-the-loop policy refinement. Common after Tier C+ launch.
Production agents on AWS Bedrock AgentCore — managed observability, action tracing, evaluation hooks, deployed inside your AWS account in us-east-1, eu-west-1 or ap-south-1 for data residency.
Tier A through E with published price bands ($15K–$300K+). Fixed-price proof-of-concepts. Fixed-scope build contracts. No hourly mystery invoices.
AgentCore for production observability, LangGraph for fine-grained graph orchestration, CrewAI for multi-agent role-playing. We benchmark on your specific task before committing to a framework.
Tool-permission scoping, human-in-the-loop checkpoints on impactful actions, audit logging, evaluation harnesses against held-out test sets. Production agents that don't do damage when they're wrong.
Planner-worker architectures route simple subtasks to Haiku or Nova ($0.002/call) and escalate planning to Sonnet or GPT-5 ($0.04/call). Typical 40–60% cost savings versus single-model setups.
SOC 2 Type II controls, HIPAA-eligible workloads on Bedrock with BAA, GDPR data residency (eu-west-1 / eu-central-1), PCI-DSS-aligned design patterns. Built in from day one, not bolted on.
Every agent project is staffed with senior AI engineers and a solution architect. Agents fail badly when junior engineers build them — we don't put juniors on agent work.
Every engagement starts with a fixed-price 2–4 week proof-of-concept on real data with one real integration. You measure accuracy and action correctness before committing to the full build.
A few of the things we deliver under ai agent development:
Free 30-minute call. We map the workflow, action surface, success metric and safety constraints. Output: a written scope, tier recommendation and price band — usually within 48 hours.
Fixed-price 2–4 week proof-of-concept on real data with the real model and one real integration. You measure accuracy and action correctness before committing to the full build.
Engineer the production agent — tool definitions, permission scoping, orchestration (AgentCore / LangGraph / CrewAI), evaluation harness, observability, human-in-the-loop. 5–20 weeks depending on tier.
Automated evaluation against a held-out test set of 100–500 scenarios scored on accuracy, action correctness and safety. Quality metrics you can show your CFO before launch.
Production deploy, observability dashboards, weekly accuracy review, monthly LLM cost optimization. Most clients move to an ongoing retainer once the agent is live.
AI agent development is the engineering of autonomous software that uses large language models to plan and complete multi-step tasks — not just answer questions. An AI agent decides what to do next, calls tools and APIs, evaluates intermediate results and stops only when the task is done. iMagic Solutions builds production AI agents on AWS Bedrock AgentCore, LangGraph and CrewAI with human-in-the-loop guardrails.
A chatbot answers questions; an AI agent takes actions. The agent decides what to do next, calls tools, checks intermediate results and acts on its own. That ability to act — not just generate text — is what defines an agent and what drives the price up: every action requires tool definitions, permission scoping, audit logging and human-in-the-loop checkpoints for impactful actions.
AI agent pricing in 2026 ranges from $15,000 for a Tier A single-action read-only agent to $300,000+ for a Tier E enterprise autonomous agent with 5+ system integrations, RBAC and compliance. A typical Tier C multi-action workflow agent costs $40K–$90K offshore-delivered and 7–10 weeks to build. See the full breakdown at /blog/ai-agent-pricing-2026.
AWS Bedrock AgentCore is our default for production agents — managed observability, tracing, evaluation, native AWS account deployment. LangGraph is the right choice for fine-grained graph-based orchestration AgentCore can't express. CrewAI is the right choice for multi-agent setups with explicit role-playing. We benchmark on your specific task before committing.
Three defenses in order of priority. First, tool-permission scoping — the agent literally cannot call APIs it doesn't have an IAM grant for. Second, human-in-the-loop checkpoints on high-impact actions — the agent drafts the email/refund/contract change but a human approves before execution. Third, evaluation harnesses that test the agent against 100–500 held-out scenarios before any prompt or model change ships.
Tier A and B agents typically pay back in 3–6 months by displacing routine lookup or single-action work. Tier C multi-action workflow agents pay back in 4–9 months by displacing meaningful operational headcount. Tier D and E pay back in 6–18 months because the up-front build is larger but they displace 5–15 FTE worth of routine knowledge work.
Yes. Every Tier B+ agent integrates with your existing systems via REST/GraphQL APIs, SDKs, webhooks or database connectors. Most-requested integrations: Salesforce, HubSpot, Pipedrive, Zendesk, Freshdesk, Intercom, Jira, Linear, Notion, Confluence, Slack, Microsoft Teams, Calendly/Cal.com, Stripe, Twilio, internal REST APIs. Each integration adds 1–5 days of build time.
Per task: $0.05–$2.00 depending on tool calls and LLM invocations. For a typical Tier C agent handling 1,000 workflows/month: $200–$800/month all-in (LLM API + tool calls + hosting + observability). Tier E enterprise agents handling 50,000+ tasks/month run at $2,000–$8,000/month. Planner-worker routing typically reduces these numbers 40–60%.
Yes. EU agent work is delivered into eu-west-1 or eu-central-1 with GDPR-compliant data flows and DPAs. US enterprise agents support SOC 2 Type II controls and HIPAA when required, deployed inside the client's own AWS account on Bedrock with the AWS BAA. Compliance is designed in from day one — PII redaction, encryption, audit logging, RBAC.
Tier A read-only: 3–5 weeks. Tier B single write-back: 5–7 weeks. Tier C multi-action workflow: 7–10 weeks. Tier D multi-system autonomous: 10–14 weeks. Tier E enterprise: 12–20 weeks. Every engagement starts with a 2–4 week fixed-price proof-of-concept first to validate accuracy on real data before committing to the full build.
Yes — multi-agent supervisor-and-specialist architectures are a Tier D/E option. A planner agent coordinates specialist sub-agents (research, write, verify, publish, for example). We build these on CrewAI, LangGraph supervisor patterns or AgentCore's native multi-agent orchestration. Typical use cases: research-and-write systems, customer-success fleets, compliance pipelines and complex content workflows.
Yes — agent rescue is a common engagement. We audit the architecture, action-correctness gaps, runaway LLM costs and evaluation holes; map a fix plan; then either patch in place or re-platform to AWS Bedrock AgentCore with the right framework. Typical rescue projects ship a stable production-ready agent in 4–10 weeks.
Book a free 30-minute discovery call via /contact. We'll walk through the workflow you want to automate, success metric, integrations and safety constraints — then send a written scope, tier recommendation and price band within 48 hours. Most engagements start within 1–2 weeks with the fixed-price proof-of-concept.
Generative AI agents, RAG assistants, copilots and chatbots built on AWS Bedrock, Claude, OpenAI and open models — for India and the USA.
LLM-powered, RAG-grounded chatbots for web, WhatsApp, Slack and Teams — from $3K rule-based FAQ bots to $150K+ enterprise AI assistants. USD pricing, US/EU/India delivery.
AWS-certified cloud architecture, migration, serverless, DevOps and FinOps cost optimisation — plus AWS Bedrock & generative AI on AWS.
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