The 9 best AI agents for coding, business automation, and IT ops

The nine best AI agents below span coding, business automation, and IT operations. They are grouped by category for engineering leaders and developers shortlisting agents for production.

Gartner predicted 40% of enterprise applications will feature task-specific AI agents by the end of 2026, from a base of less than 5%. Adoption has outrun production. Forrester finds three-quarters of enterprise leaders report adopting agentic AI, while "only a small minority" run it in meaningful production.

TL;DR:

• Nine agents, three categories: Six coding agents (Claude Code, Codex, Antigravity CLI, Cursor, GitHub Copilot, Devin) plus Salesforce Agentforce, Microsoft Copilot Studio, and ServiceNow Otto for business and IT ops.
• Adoption outpaces production: Three-quarters of enterprise leaders report adopting agentic AI, but only a small minority run it in meaningful production.
• Coding agents lead autonomy: Claude Code and Devin handle multi-hour, multi-file work with minimal human input. Copilot and Cursor stay closer to developer-in-the-loop patterns.
• Platform agents stay inside ecosystems: Agentforce, Copilot Studio, and Otto run on their vendor's data and governance. Value scales with existing platform investment.
• Execution infrastructure is half the decision: Where agent code runs determines cost, isolation, and reliability. Resume speed, state persistence, and microVM isolation separate production setups from demos.

How the nine best AI agents compare

What are AI agents?

An AI agent directs its own processes and tool use to finish a task. A chatbot answers questions. A copilot suggests code but needs continuous developer input. An agent runs a loop: plan, act, observe results, adjust, repeat. That loop traces back to the ReAct paper (Yao et al., ICLR 2023). It interleaves reasoning traces with actions and observations. The Model Context Protocol (MCP) is an open protocol standardizing how large language model (LLM) applications connect to external tools.

1. Claude Code

Claude Code is Anthropic's terminal-native coding agent, now running Claude Opus 5 and Sonnet 5. On current models, the 1M-token window is the default context window, with no long-context price premium. Anthropic's highest published SWE-bench Verified score remains Claude Opus 4.7's 87.6%.

Dynamic Workflows orchestrate tens to hundreds of agents in the background. The changelog documents sub-agents nested five levels deep and a /loop command for recurring prompts. Plugins, skills, and hooks package extensions across projects. Self-hosted runners turn your machines into execution targets.

Claude Code's main constraint is token pricing, which needs budget modeling. Sonnet 5 pricing is $2 input and $10 output per million tokens, now permanent. Opus 5 runs $5 / $25. Cross-vendor comparisons need care, because OpenAI stopped reporting SWE-bench Verified over contamination concerns.

Which teams should use Claude Code?

Use Claude Code for complex multi-file refactors and long-running background work in large repositories.

2. OpenAI Codex

OpenAI Codex is a coding agent spanning CLI, IDE extension, desktop app, and cloud. OpenAI reports that Codex has more than 5 million weekly active users. Codex runs the GPT-5.6 family: Sol (detail and polish), Terra (balanced), and Luna (cost-efficient).

The desktop app runs agents in separate threads by project. OpenAI announced an in-app browser for frontend work, and Codex's changelog records ChatGPT Voice inside Codex. In research preview, Codex Security scans 30 million+ commits across 30,000+ codebases.

As of December 2025, OpenAI reported that AGENTS.md configuration files were used in 60,000+ projects.

ChatGPT Plus starts at $20/month. API pricing runs $5 / $30 per million tokens for Sol down to $0.20 / $1.20 for Luna. One constraint stands out: new ChatGPT Business plans can't add Codex seats.

Which teams should use OpenAI Codex?

Pick Codex if your team already runs on ChatGPT and wants parallel agents on every surface.

3. Google Antigravity CLI (successor to Gemini CLI)

Google retired Gemini CLI for individual and Google AI Pro/Ultra users and replaced it with Antigravity CLI. Built in Go, Antigravity CLI keeps Agent Skills, Hooks, Subagents, and Extensions, now called Antigravity plugins. Antigravity CLI orchestrates multiple agents asynchronously and shares its agent harness with the Antigravity 2.0 desktop app.

Model backing centers on Gemini 3.5 Flash, which Google co-developed using Antigravity. The pricing page also lists Gemini 3.1 Pro, Claude Sonnet & Opus 4.6, and gpt-oss-120b. A $0 individual tier includes unlimited Tab completions and Command requests. Paid plans run $20, $100, and $200 monthly. The clearest limit is that enterprise access excludes the Claude and gpt-oss models. Gemini Code Assist licensees keep the old CLI under daily caps, and the Apache 2.0 repo stays community-maintained.

Which teams should use Antigravity CLI?

Google-stack teams should choose Antigravity CLI for a multi-model terminal agent with a working free tier.

4. Cursor

Cursor is an AI-first IDE, now owned by SpaceX after a $60 billion all-stock acquisition. Cursor 3.0 added an Agents Window for running many agents in parallel. Build in Parallel splits a plan into independent parts and runs them with async subagents. Cursor says the first-party Composer 2.5 model improved long-running tasks and costs $0.50 input / $2.50 output per million tokens.

Cursor's model documentation lists the GPT-5.6 family, Claude Opus 5 and Sonnet 5, Gemini 3.1 Pro, and Grok 4.6. Individual plans run Hobby (free), Pro at $20, and Ultra at $200 monthly.

Teams pricing starts at $40 per user monthly. Cursor's main cost limit is that third-party models bill against usage pools at API rates, so heavy Opus or Sol use drains allowances fast. What you pay depends less on the plan than on which third-party models your team runs.

Which teams should use Cursor?

Choose Cursor for parallel agent execution without leaving a visual IDE.

5. GitHub Copilot

GitHub Copilot's coding agent runs agent tasks on branches and in issues inside the existing GitHub workflow. Billing moved to token-based GitHub AI Credits, and a Copilot Max plan at $100/month. Max includes 10,000 base credits plus a 10,000-credit flex allotment for sustained agent workloads. Business is $19 and Enterprise $39 per user monthly.

The cloud agent added semantic code search, deep research, and implementation-plan generation. Scheduled and event-triggered automations run agent tasks without a human trigger, auto model selection carries a 10% discount, and reasoning levels are configurable.

Copilot's budgeting limit is that the flex allotment is variable, "designed to adapt as the economics of AI evolve," which softens predictability.

Which teams should use GitHub Copilot?

GitHub-native organizations should run Copilot on contained tasks like bug fixes and test coverage.

6. Devin

Cognition's Devin is now a product suite: Devin Cloud, Devin Desktop, Devin CLI, and Devin Review. Devin Desktop is the renamed Windsurf IDE with an Agent Command Center. The CLI runs locally and can hand a session off to cloud-based Devin. That cloud session keeps working in a sandboxed VM after you close your laptop. Interactive Planning proposes a plan, then waits 30 seconds for feedback. Large tasks fan out to parallel managed Devins in isolated VMs.

Pricing dropped from a $500/month Team entry point to self-serve plans. Those are Free, Pro at $20/month, and Max at $200/month. Teams is usage-based with an $80 minimum, and Enterprise runs on custom Agent Compute Unit (ACU) billing. Devin's pricing limit is that no public per-ACU rate exists, so enterprise cost modeling requires a sales conversation. Self-serve overage bills in dollars at API pricing, not ACUs.

Which teams should use Devin?

Delegate well-specified implementation tasks to Devin end to end, from terminal or cloud.

7. Salesforce Agentforce

Salesforce Agentforce runs autonomous agents directly on customer relationship management (CRM) data: resolving cases, qualifying leads, updating records. Salesforce reports 2.6 million Agentforce support conversations at a 63% autonomous resolution rate.

A Forrester Total Economic Impact study found 396% three-year return on investment (ROI) with payback under six months. In the author's assessment, the adoption curve holds for Salesforce shops, but the economics assume you already own the CRM.

Pricing is usage-based. Agent action pricing is about $0.10 per action via Flex Credits, at $500 per 100,000 credits. Alternatives are $2 per conversation or Agentforce 1 Editions at $550 per user monthly. In the author's assessment, Agentforce's adoption limit is onboarding: teams outside the Salesforce ecosystem face a steep climb, and per-action costs rise with volume.

Which teams should use Agentforce?

Enterprises already on Salesforce should deploy Agentforce for agents grounded in live CRM data.

8. Microsoft Copilot Studio

Microsoft Copilot Studio is a low-code builder for agents across Microsoft 365 and Azure. Billing uses Copilot Credits at Copilot Studio pricing of $200 per 25,000-credit monthly pack, billed annually. Microsoft's credit rates run 1 credit per classic answer, 2 per generative answer, and 5 per agent action.

The Agent-to-Agent (A2A) protocol reached general availability, and agent evaluations shipped. Every new agent now requires a Microsoft Entra Agent ID. Microsoft reported that nearly 90% of the Fortune 500 had active agents built with its low-code tools. That install base may make Copilot Studio more familiar to some enterprise governance teams. Compliance covers SOC, ISO 27001, and FedRAMP.

The Health Insurance Portability and Accountability Act (HIPAA) is covered under a business associate agreement. In the author's assessment, Copilot Studio fits human-in-the-loop internal workflows best. Credit consumption varies by answer type, which complicates forecasting.

Which teams should use Copilot Studio?

Microsoft 365 enterprises should build in Copilot Studio to automate internal workflows under strict governance.

9. ServiceNow AI agents (now ServiceNow Otto)

ServiceNow rebranded its AI experience from Now Assist to ServiceNow Otto in its latest release, with entitlements unchanged. Otto agents run inside ServiceNow against the customer's existing platform data. Supported models include GPT-5.4, Gemini 3.5 Flash, Claude 4.6 Sonnet, and the Now LLM Service. Third-party models are now the default for Otto for EA skills.

ServiceNow reported that its AI contract value crossed $1 billion, with agentic deployments growing 9× in nine months. Growth that fast lands on per-assist metering, so model your ticket volume before signing.

Packaging moved to new packaging tiers called Foundation, Advanced, and Prime, with no separate AI purchase. Consumption is metered per assist. Assist consumption runs 25 to 150 assists per agentic workflow, depending on size. Otto's model limit is that some out-of-box agents don't support bring-your-own LLM.

Which teams should use ServiceNow Otto?

Large enterprises standardized on ServiceNow should use Otto for agent work inside the platform they already run.

How to choose execution infrastructure for AI agents

Choosing the agent is half the decision. Where its code executes is the other half. In the author's assessment, platform agents (Agentforce, Copilot Studio, ServiceNow Otto) run inside their vendor's ecosystem, so the vendor makes execution decisions for you. Coding agents like Claude Code, Codex CLI, Devin CLI, and Antigravity CLI run on infrastructure you control.

Why execution infrastructure matters for AI agents

For agents that execute code, infrastructure choices show up directly in cost and reliability. A study of agent harnesses measured idle CPU more than 80% of the time. Memory sat idle more than 50%. Tearing down sandboxes after each tool call cut sandbox cost 65%. Repeated snapshot capture and restore raised total customer cost 4.9×. Isolation matters as much for AI-generated code.

NIST SP 800-190 notes containers "do not offer as clear and concrete of a security boundary as a VM." The New Stack reports microVM isolation is emerging as a de facto standard for protecting hosts from AI-generated code. Idle waste and isolation gaps together explain why setups that pass a demo fail a production pilot.

What to look for in agent execution infrastructure

Four capabilities separate production-grade execution from demo setups:

• Resume speed: The time between requesting compute and having an environment ready. Interactive coding agents stall visibly when that wait runs into seconds.
• State persistence: Whether filesystem, memory, and running processes survive between invocations. Without persistence, agents repeat expensive setup like re-cloning repositories.
• Security isolation: Hardware-enforced boundaries for untrusted, AI-generated code. MicroVMs run a separate kernel per workload; containers share the host kernel.
• Cost predictability: Paying only for active compute, without minimum billing periods or idle charges.

Score any candidate runtime on all four before you commit an agent workload to it.

How Blaxel handles agent execution

Perpetual sandbox platforms like Blaxel target these four requirements. In the author's assessment, teams running low-volume internal tooling or GPU inference workloads won't feel these constraints.

The calculus changes when agents execute untrusted code for real users. Blaxel Sandboxes stay in standby indefinitely at zero compute cost, then resume in under 25ms with filesystem and memory intact. After 15 seconds of network inactivity, a sandbox returns to standby. MicroVM isolation runs AI-generated code against its own kernel rather than the host's.

Agent Drive, in private preview, is a distributed filesystem that multiple sandboxes mount at once with concurrent read-write access. That gives agents and sessions co-located shared storage for context, artifacts, and data. Volumes provide persistent block storage for long-term data. Batch Jobs handle scheduled and fan-out background work. The MCP Hub offers 100+ prebuilt tool integrations.

Every sandbox exposes a built-in MCP server so agents can operate it through tool calls. Blaxel is also a first-class sandbox provider in the OpenAI Agents SDK. Model Gateway provides unified model access, telemetry, and token cost controls for LLM routing. Networking includes managed custom domains and proxy secrets injection, while dedicated egress gateways are in private preview.

Pricing

• Free: Up to $200 in free credits plus usage costs
• Pre-configured sandbox tiers and usage-based pricing: See Blaxel's pricing page for the most up-to-date pricing information
• Available add-ons: Email support, live Slack support, HIPAA compliance

Start free or book a demo to test Blaxel against your agent architecture.

FAQs about the best AI agents

Can AI agents replace developers?

No. A METR randomized trial found experienced open-source developers took 19% longer on issues when allowed AI tools. Those same developers had predicted a 24% speedup. Stack Overflow's developer survey found 64% of developers don't perceive AI as a job threat, while 46% distrust AI accuracy. The DORA report frames AI as an amplifier. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones.

How should agents that execute code be permissioned?

Grant each agent the minimum tools its task requires, with per-tool scoping (read-only versus write, specific resources). OWASP's excessive agency guidance recommends implementing authorization in downstream systems rather than letting the LLM decide what's allowed. OWASP's agent security cheat sheet classifies execute_code as high risk. Code-executing agents belong in hardware-isolated environments with scoped, short-lived credentials.

What's the difference between a coding agent and a copilot?

A 2025 Google Research paper sorts products, not brands. It puts GitHub Copilot and Cursor's assist features on the copilot side. Devin, OpenHands, and Claude Code sit on the agent side. Most copilot products now ship agent modes, so evaluate the mode.