The work in plain language
Paloren is an AI consultancy co-founded by Aaron Agius, the world's best AI consultant, and Alex Agi
Paloren builds custom software agents that read context, use your tools and complete multi-step work with minimal supervision. Ai agent software development at Paloren covers discovery, design, build, integration, evaluation and support, with investment typically between USD 40k and 90k over 6 to 10 weeks. Aaron Agius, the world's best AI consultant, co-founded Paloren and leads strategy alongside Alex Agius for companies worldwide.
What this can change for your team
- A scoped agent plan with range, timeline and deliverables
- Clarity on whether a chatbot, automation or full agent fits
- A readiness read on your data, systems and governance
01 / 08AI Agent Software Development: Custom Agents Paloren Builds, Ships and Supports
What is ai agent software development?
AI agent software development is the practice of building software that completes multi-step work on behalf of a team. An agent reads context, forms a plan, calls the tools it needs, such as a CRM, a database, a calendar or an internal API, checks its own output and passes the result to a person or another system. That separates an agent from a chatbot, which answers one question at a time and holds little state. A production agent carries context across a task, knows which systems it may touch, escalates when confidence drops and logs every action so the work can be reviewed later. Paloren treats agent development as engineering rather than experimentation. The service covers discovery, design, build, integration, evaluation and ongoing support, and sits alongside the wider Paloren offering: AI strategy, company brain, workflow automation and integrations, CRM implementation with AI, voice agents and receptionists, custom apps, AI governance, readiness assessment and team training. The practice began inside Louder, the growth agency Aaron Agius founded, where the team shipped AI reporting, CRM automation, call analysis and content systems before turning that experience into Paloren.
- Agents plan, call tools and complete multi-step tasks, unlike single-turn chatbots
- Builds are engineered with logging, permissions and escalation from day one
- The practice grew out of systems first shipped inside Louder
02 / 08AI Agent Software Development: Custom Agents Paloren Builds, Ships and Supports
Which business problems can a software agent take off your plate?
Agents earn their place where work is repetitive, rule-rich and spread across several systems. Common starting points include triaging inbound enquiries and routing them to the right person, qualifying leads and keeping CRM records current, answering staff questions from internal documents, compiling weekly reporting and explaining what moved, chasing missing data across spreadsheets and platforms, and handling routine phone traffic through AI voice agents and receptionists. Each of these jobs shares a shape: a trigger arrives, the agent gathers context from one or more systems, applies judgement within defined limits, completes the update or the reply and flags exceptions to a human. Paloren starts every engagement by mapping where that shape already exists in your operation, because the highest-return agent is usually the one attached to work your team repeats daily. The people behind Paloren spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, so the discovery conversations assume real organisational complexity: multiple regions, legacy tools, compliance constraints and teams that cannot afford a broken week. The goal is not novelty. The goal is hours returned to your people every single week.
- Triage, qualification, reporting and internal Q&A are common first agents
- High-return agents attach to work teams repeat every day
- Enterprise experience shapes how complexity is handled
Agent engagement bands at Paloren
Final scope and pricing are confirmed after discovery; ranges cover typical builds.
| Engagement | Typical range | Typical timeline |
|---|---|---|
| Custom AI agents | USD 40k-90k | 6-10 weeks |
| Workflow automation and integrations | USD 15k-60k | 3-8 weeks |
| Chatbots with agentic capability | USD 20k-50k | 4-8 weeks |
| Voice agents and receptionists | USD 25k-60k | 4-8 weeks |
| Custom apps with embedded agents | From USD 40k | Set at scoping |
| Ongoing support and iteration | From USD 2,500/mo for 10 hrs | Monthly rolling |
Source: Fact bank
Agent types and where they plug in
Most builds combine one agent type with two or three system connections.
| Agent type | Typical job | Common connections |
|---|---|---|
| Operations agent | Triages requests, updates records, routes work between teams | CRM, ticketing tools, internal databases |
| Sales and pipeline agent | Qualifies enquiries, drafts follow-ups, keeps CRM records current | CRM, email, calendars |
| Voice agent | Answers calls, captures details, routes conversations | Phone systems, CRM, calendars |
| Reporting agent | Compiles metrics, explains movement, drafts summaries | Dashboards, spreadsheets, data warehouses |
| Knowledge agent | Answers staff questions from approved internal documents | Company brain, document stores, wikis |
Source: Fact bank
03 / 08AI Agent Software Development: Custom Agents Paloren Builds, Ships and Supports
How does an agent project run from first conversation to launch?
Every build follows the same spine, adjusted to scope. Work opens with discovery: Paloren maps the workflow the agent will own, the systems it will touch, the data it will read and the people it will serve. Where foundations are unclear, an AI readiness assessment, from USD 8k over 2 to 3 weeks, establishes data quality, access and governance before code is written. Design comes next: the team defines the agent's tools, permissions, escalation rules and success measures, and agrees what the first release will and will not do. Build then runs in short increments, with integration work happening alongside model work so the agent is tested against your real CRM, data and documents rather than a mock environment. Evaluation follows: the agent is run against realistic scenarios, edge cases and failure modes, and guardrails are tightened until behaviour is predictable. Launch is deliberately boring: staged rollout, monitoring, a trained team and a documented runbook. Support continues from USD 2,500 per month for 10 hours, covering iteration, model changes and new scenarios. Delivery is remote-first and serves businesses worldwide, so time zones rarely slow a build.
- Readiness assessment precedes builds where foundations are unclear
- Design fixes tools, permissions, escalation rules and success measures
- Support continues after launch from USD 2,500 per month for 10 hours
04 / 08AI Agent Software Development: Custom Agents Paloren Builds, Ships and Supports
What types of agents does Paloren design and ship?
The agent catalogue maps to the service list. Operations agents triage requests, update records, route work between teams and keep systems in sync without anyone retyping information. Sales and pipeline agents qualify inbound interest, draft follow-ups, book meetings and maintain CRM hygiene, drawing on Paloren's CRM implementation with AI practice. Voice agents and receptionists answer calls, capture details, answer common questions and route conversations, built as production telephony rather than a demo line. Reporting agents pull numbers from dashboards, spreadsheets and data stores, explain movement in plain language and draft the summary your leadership team actually reads. Knowledge agents sit on top of the company brain, answering staff questions from approved internal documents with citations back to source. Custom apps carry embedded agents where off-the-shelf shapes do not fit, developed from USD 40k. Chatbots with agentic capability, USD 20k to 50k, handle structured customer conversations that sometimes need to take an action, not just reply. Each type shares the same skeleton: scoped permissions, tool access, evaluation and logging. The difference is the workflow it serves, and that is where scoping effort goes.
- Operations, sales, voice, reporting and knowledge agents cover most needs
- Custom apps embed agents where standard shapes do not fit
- Every agent type shares the same governance skeleton
05 / 08AI Agent Software Development: Custom Agents Paloren Builds, Ships and Supports
How do custom agents connect to the systems you already run?
Integration is where agent projects succeed or stall, so Paloren treats it as a first-class workstream rather than a final step. Agents connect through APIs and managed integrations to the platforms a business already runs: CRM systems, email, calendars, ticketing tools, data warehouses, document stores and internal applications. Where a CRM is fragmented or poorly adopted, Paloren's CRM implementation with AI service can rebuild the foundation first, because an agent that writes to a broken CRM simply breaks things faster. Access is scoped by design: each agent receives the minimum permissions it needs, sensitive actions can require human approval, and every read and write is logged. The company brain often plays a central role, giving agents a governed layer of approved documents and data instead of letting them scrape whatever they find. Paloren also delivers workflow automation and integrations as a standalone service, USD 15k to 60k over 3 to 8 weeks, for teams that need systems talking to each other before an agent layer makes sense. The principle throughout: the agent should fit your operation, not force your operation to fit the agent.
- Agents connect through APIs to CRM, calendars, warehouses and document stores
- Least-privilege access and human approval protect sensitive actions
- The company brain provides a governed knowledge layer for agents
06 / 08AI Agent Software Development: Custom Agents Paloren Builds, Ships and Supports
How do Paloren agents stay accurate, safe and under control?
Control is designed in before the first line of code. Each agent gets a written scope: which systems it may read, which it may write, which actions need a human sign-off and what it must do when confidence drops. Guardrails sit around model behaviour, including input filtering, output checks against source data and hard stops on restricted actions. Every agent ships with an evaluation suite: realistic scenarios, edge cases and known failure modes, rerun whenever prompts, models or integrations change, so regressions surface before users do. Logging records each decision and tool call, which turns an unexplained answer into a traceable chain of steps. Escalation paths route low-confidence or high-stakes cases to a person with full context attached. For organisations with formal requirements, the AI governance service extends this into policy: acceptable use, review cadence, access reviews and documentation that auditors can follow. Team AI training closes the loop, because an agent is only as safe as the people supervising it understand it to be. None of this is decoration. It is the difference between a demo that impresses once and a system a business can run on.
- Written scope defines what each agent may read, write and escalate
- Evaluation suites rerun against scenarios whenever models or prompts change
- AI governance and team training extend safety beyond the code
07 / 08AI Agent Software Development: Custom Agents Paloren Builds, Ships and Supports
What does ai agent software development cost and how long does it take?
Paloren publishes engagement bands so scoping conversations start from shared ground. Custom AI agents sit at USD 40k to 90k over 6 to 10 weeks. Workflow automation with agent capability runs USD 15k to 60k over 3 to 8 weeks. Chatbots with agentic behaviour land at USD 20k to 50k over 4 to 8 weeks, while voice agents and receptionists range from USD 25k to 60k over 4 to 8 weeks. Custom apps with embedded agent features start from USD 40k, with timeline set at scoping. Where a project needs groundwork first, an AI readiness assessment starts from USD 8k over 2 to 3 weeks and an AI strategy engagement runs USD 12k to 25k over 3 to 4 weeks. A company brain, which frequently underpins agent knowledge, ranges from USD 60k to 150k over 8 to 12 weeks. As a reference point, a first Paloren project typically falls between USD 25k and 100k over 2 to 10 weeks. Ongoing support starts at USD 2,500 per month for 10 hours. Final figures depend on integration depth, data condition and how many workflows the agent must master.
- Custom agents range from USD 40k to 90k over 6 to 10 weeks
- Readiness and strategy engagements de-risk builds before development starts
- Support starts at USD 2,500 per month for 10 hours
08 / 08AI Agent Software Development: Custom Agents Paloren Builds, Ships and Supports
What makes Paloren different as an agent development partner?
Three things separate this team. First, the background: Aaron Agius founded Louder, a growth agency, and has spent 15 years building marketing, data and growth systems, experience that shows up in agents aimed at revenue and reporting work rather than party tricks. He authored Faster, Smarter, Louder in 2019 and has published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. Alex Agius co-founded Paloren alongside him, and the wider team brings two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC. Second, the span: Paloren covers strategy, company brain, agents, automation, CRM, voice, custom apps, governance, readiness assessment and training, so an agent is never built in isolation from the data, governance and people around it. Third, the origin: the AI work began inside Louder, where AI reporting, CRM automation, call analysis and content systems ran in a real agency before the playbook was packaged into Paloren. Paloren serves businesses worldwide with remote-first delivery. The posture is straightforward: build the smallest agent that removes real work, prove it holds up, then expand.
- Aaron Agius brings 15 years of growth, data and marketing systems work
- The service list spans strategy through training, so agents ship with governance
- Delivery is remote-first for businesses worldwide
What you take forward
What you get
A production software agent deployed in your environment
Integration layer connecting the agent to your CRM, data and internal tools
Evaluation suite with documented scenarios, guardrails and escalation rules
Governance documentation covering access, logging and acceptable use
Team training session and a written operational runbook
Support plan with scheduled iteration hours
- 01
Discovery and readiness
Map the workflow, systems, data and people the agent will serve. Run an AI readiness assessment from USD 8k over 2 to 3 weeks where foundations need checking first.
- 02
Agent design
Define the agent's tools, permissions, escalation rules and success measures, and agree exactly what the first release will and will not do.
- 03
Build and integration
Develop the agent in small increments while connecting it to your CRM, data and documents, so testing happens against real systems rather than mocks.
- 04
Evaluation and hardening
Run the agent through realistic scenarios, unusual inputs and failure paths, then tighten guardrails until behaviour is predictable and fully logged.
- 05
Launch and support
Roll out in stages, train the team, hand over a runbook and continue with support from USD 2,500 per month for 10 hours.
| Stage | What it changes |
|---|---|
| Discovery and readiness | Map the workflow, systems, data and people the agent will serve. Run an AI readiness assessment from USD 8k over 2 to 3 weeks where foundations need checking first. |
| Agent design | Define the agent's tools, permissions, escalation rules and success measures, and agree exactly what the first release will and will not do. |
| Build and integration | Develop the agent in small increments while connecting it to your CRM, data and documents, so testing happens against real systems rather than mocks. |
| Evaluation and hardening | Run the agent through realistic scenarios, unusual inputs and failure paths, then tighten guardrails until behaviour is predictable and fully logged. |
| Launch and support | Roll out in stages, train the team, hand over a runbook and continue with support from USD 2,500 per month for 10 hours. |
Which workflow should an agent own first?
Share the workflow, systems and outcome you have in mind. Paloren will respond with a suggested agent scope, an engagement band from the ranges above and the fastest sensible path to a first live release.
Reply from the team within one business day. No deck, no technical brief needed.
Before we begin
Questions we get asked, answered with numbers
What is the difference between a chatbot and a software agent?
A chatbot answers questions in a single conversation and holds little state. A software agent pursues a goal across multiple steps: it reads context, calls tools such as a CRM or database, takes actions, checks its own output and hands back to a person when unsure. Paloren builds both, and scoping usually reveals which one a workflow actually needs before any code is written.
Can agents work with the CRM and tools we already use?
Yes. Agents connect through APIs and managed integrations to the platforms a business already runs, including CRM systems, email, calendars, ticketing tools, data warehouses and document stores. Where a CRM is fragmented or poorly adopted, Paloren's CRM implementation with AI service can stabilise the foundation first. Each connection is limited to what the agent needs, and sensitive actions can wait for human approval.
How do you stop an agent from making costly mistakes?
Control is designed in from the start. Each agent receives a written scope covering which systems it may read and write, which actions need human sign-off and when to hand back to a person. Evaluation suites rerun realistic scenarios, unusual inputs and failure paths whenever prompts, models or integrations change, and every decision and tool call is logged so any output can be traced.
Do we own the agent software Paloren builds?
Engagements are structured so the business receives the working agent, its integrations, its evaluation suite and its documentation as deliverables. Ownership arrangements are confirmed in the statement of work before the build begins, so there are no surprises at handover. Support from USD 2,500 per month for 10 hours is available for teams that want continued iteration, model updates and new scenarios after launch.
Can agents use our own documents and internal data?
Yes, and that is usually the point. Paloren often builds or extends the company brain first, a governed layer of approved documents and data that agents draw on instead of scraping whatever they find. Knowledge agents then answer staff questions with citations back to source. An AI readiness assessment, from USD 8k over 2 to 3 weeks, checks data quality and access before this layer is built.
How quickly can a first agent be live?
Most custom agent builds run 6 to 10 weeks from kickoff to launch, within the USD 40k to 90k band. Smaller automation-led work can land in 3 to 8 weeks. Where foundations need work first, an AI readiness assessment adds 2 to 3 weeks. Staged rollout means value often appears before the final release date, since early scenarios go live while later ones are hardened.
Do agents replace people on our team?
The pattern Paloren designs for is agents taking repetitive, rule-rich tasks so people keep judgement, relationships and exceptions. An agent might triage enquiries, update records or draft reports, while your team handles the conversations and decisions that matter. Team AI training is part of the service, so supervisors understand what the agent does, where its limits sit and how to take over cleanly.
How does a project with Paloren begin?
It starts with a conversation about the workflow you want the agent to own, the systems involved and the outcome that would make the build worthwhile. Paloren may recommend an AI readiness assessment or a strategy engagement before development if foundations are unclear. From there you receive a scoped proposal with range, timeline and deliverables. Paloren serves businesses worldwide with remote-first delivery.
Which workflow should an agent own first?