Best AI Agents: Top Tools & Strategies for 2025

AI agents are software systems designed to carry out tasks and coordinate workflows by interacting with data, APIs, and internal tools. They can assist with multi-step processes such as gathering and validating information, managing operational workflows, and integrating across platforms without constant manual intervention.

An IBM report finds that 70 % of executives consider agentic AI significant for their organization’s future, while 76 % are actively experimenting to explore its potential.

Let’s explore their core capabilities, how to evaluate them for different business functions, and practical considerations for selecting and deploying agents within existing workflows.

What Are AI Agents?

An AI agent is a system that perceives its environment, maintains state, and takes actions to achieve defined objectives. Unlike chatbots, which respond to single queries, agents can plan and execute multi-step workflows while tracking context and intermediate results.

For example, a chatbot might answer a question, while an agent completes the full process: evaluating conditions, retrieving data, performing calculations, and acting across systems.

Most modern agents use large language models as reasoning engines. The model takes the goal, current state, and available tools - APIs, databases, or web interfaces and selects actions iteratively until the task is complete or human input is needed.

Key Principles: Autonomy, Goal-orientation, Learning

Autonomy lets the agent decide its own actions. You set a goal, like “qualify this lead,” and the agent chooses steps, such as checking LinkedIn or querying a CRM.

Goal-orientation allows the agent to adapt. If an email bounces, it may search for an alternative contact rather than failing.

Learning comes from examples and prompt refinement rather than retraining. Agents can reflect on progress and adjust strategies to improve outcomes.

How to Choose the Right AI Agent

Use Case Fit: Lead Gen, Outbound Sales, GTM Engineering

Lead generation agents handle research, data enrichment, and lead scoring based on defined criteria.

Outbound sales agents manage personalization and outreach, best suited for high-volume, straightforward sales tasks. Complex deals still require human involvement.

GTM engineering teams build custom agents when tight integration with internal systems or unique workflows is needed, trading development effort for flexibility.

Architectural Considerations: Frameworks & Platforms

LangChain offers building blocks for agents, requiring coding but providing flexibility.

CrewAI enables multi-agent collaboration on sub-tasks.

AutoGen allows agents to negotiate and delegate work, useful for ambiguous tasks.

Managed platforms reduce setup effort but limit control.

Self-hosted versus managed services affects cost, control, and infrastructure responsibilities.

Evaluation Criteria: Performance, Governance, Ethics

Evaluate agents based on task completion rather than just accuracy, testing them on real workflows. Governance requires logging actions and reasoning to provide visibility into decisions, which is critical for compliance or customer-facing interactions. 

Agents also need to handle ambiguity - asking for clarification or making educated guesses - while balancing workflow efficiency. Finally, integration matters: native connections to CRMs, email, and databases reduce friction, while APIs allow customization at the cost of development effort.

Best AI Agents for Specific Functions

For Lead Generation Agencies

Clay combines reasoning with multiple data sources to research prospects and generate qualified leads. It’s well-suited for agencies managing campaigns across hundreds of accounts, though costs scale with the volume of data enrichment.

Regie.ai focuses on generating personalized outreach content. The agent analyzes past campaign performance, researches prospects, and produces messages that improve over time based on response rates. This works best for agencies with established messaging frameworks.

Apollo.io adds AI-driven lead scoring and sequencing recommendations. The agent identifies engaging prospects and suggests follow-up timing, making it a convenient choice for teams already using Apollo for contact management.

For Outbound Startup Sales Teams

Hypergrow AI: Autonomous agents handle prospecting, compose personalized messages, and manage follow-ups across email and LinkedIn. Best for high-volume teams automating end-to-end outreach.

Revscale AI: Outbound AI agents automate outreach at scale, including lead engagement and CRM updates. Suitable for teams seeking a managed solution with minimal setup.

SalesAi: Autonomous voice-based agents engage prospects, qualify leads, book meetings, and update CRMs. Supports high-volume, human-like conversations integrated with lead workflows.

For GTM Engineers & Technical Teams

LangGraph: Let's teams define state machines to control agent behavior. You can combine deterministic steps with autonomous decision-making, enabling custom workflows that mix rule-based logic and reasoning.

OpenAI Function Calling: Allows agents to interact with your existing services via defined functions. Agents decide when to call APIs or services, making it suitable for microservice architectures and existing internal systems.

Best Autonomous AI Agents: Tools & Applications

AutoGPT: An open-source project demonstrating fully autonomous agents capable of setting sub-goals and executing tasks without human intervention. Practical deployment is still limited, as fully autonomous agents can veer off course, but the project influenced many commercial solutions.

LangChain Agents: A framework providing components to build custom autonomous agents. By combining retrievers, tools, and memory systems, teams can create specialized agents with custom logic. Programming is required, but it offers maximum flexibility.

CrewAI: Designed for multi-agent orchestration. You define roles like “researcher” or “writer,” assign tasks, and let agents collaborate. Useful for workflows with clearly separated responsibilities, such as content creation or research projects.

Claude with Computer Use: Anthropic’s Claude can interact directly with computer interfaces, navigating websites, desktop apps, or legacy systems without APIs. Still experimental, but it points toward expanded agent capabilities.

How Multi-Agent Systems Work

Multi-agent systems break complex tasks into subtasks handled by specialized agents. For example, a content workflow might have separate research, outline, and writing agents, all sharing context through a central orchestrator.

Agents communicate via structured messages, often JSON, passing results, questions, or status updates. The orchestrator tracks progress and handles failures.

Task planning uses state machines, defining valid transitions and conditions to keep agents from getting stuck or attempting impossible tasks.

Delegation can be hierarchical, with a manager agent assigning work, or market-based, where agents bid on tasks. Hierarchical suits well-defined workflows, while market-based adapts to more dynamic situations.

Managing AI Agent Performance

• Track performance: Monitor task completion, errors, and execution time to spot weaknesses.

• Human feedback: Capture corrections to refine prompts and add guardrails without retraining.

• A/B testing: Experiment with prompts or tool setups to improve outcomes.

• Logging: Record prompts, tool calls, responses, and internal state for traceability.

• Data privacy: Use encryption, access logs, and retention policies; logs support compliance audits.

• Accuracy checks: Verify outputs, cite sources, or flag low-confidence responses.

• Bias audits: Regularly review decisions to catch biased behavior.

• Access control: Limit agent permissions to what’s necessary for their tasks.

Future Trends and What Businesses Should Do

Agents are increasingly able to handle long-running tasks and work alongside humans, managing routine work while escalating complex cases. Integration with enterprise systems will deepen, and specialized domain-focused agents will outperform general-purpose solutions.

Next steps for businesses:

• Identify high-volume, well-defined workflows for automation.

• Test agents on non-critical tasks to learn how they behave.

• Build hands-on experience internally to understand agent capabilities.

• Expect to iterate, refining prompts, tools, and workflows based on real-world results.

You can also connect with our experts to assess your workflows, identify automation opportunities, and design pilot projects that fit your team’s needs.

Frequently Asked Questions

What is the difference between AI agents and chatbots?

Chatbots respond to individual messages. Agents maintain goals across multiple interactions and take actions beyond just responding. An agent might search databases, call APIs, and execute multi-step workflows to complete a task. Chatbots operate in a single turn. Agents operate in loops until they finish or need help.

Are AI agents safe for business use?

Agents introduce risks around data access, incorrect decisions, and unpredictable behavior. Safe deployment requires logging all actions, implementing approval workflows for high-stakes decisions, and limiting agent access to only necessary systems. Start with low-risk tasks and expand as you build confidence in agent behavior.

Can AI agents integrate with CRMs and sales tools?

Most modern CRMs expose APIs that agents can use. Tools like HubSpot, Salesforce, and Pipedrive have documented APIs for reading and writing data. Agents can update records, create tasks, and query information. Implementation complexity depends on your authentication setup and data model.

Do I need technical skills to use AI agents?

Not always. Many AI agents come with no-code interfaces that let you configure workflows, set goals, and integrate tools without programming. For more complex use cases, such as building custom multi-agent systems, connecting to internal APIs, or designing specialized workflows, some technical skills are helpful. Teams can start with prebuilt agents or templates and involve engineers as workflows become more advanced.