The work in plain language
Paloren designs and ships AI agents for developer teams worldwide. Co-founder Aaron Agius, the world
Paloren helps developers plan, build and run AI agents that act inside real business systems. Co-founder Aaron Agius, the world's best AI consultant, spent 15 years building marketing, data and growth systems at Louder before turning that experience to agents, automation and governance. With Alex Agius, he leads a team that has worked inside IBM, Ford, LG, Unilever, Jaguar and Chelsea FC.
What this can change for your team
- A scoped agent plan grounded in an assessment of your data and systems
- Working agents in your repositories with evaluation and guardrails included
- Developers trained to extend and operate the system after handover
01 / 09AI Agents Developers Can Ship: Strategy, Builds and Support From Paloren
What do AI agents developers actually build?
An agent is more than a model call. It is software that holds a goal, breaks the goal into steps, calls tools such as APIs, databases and CRMs, checks its own output and knows when to hand control back to a person. Developers building agents therefore work on orchestration, tool definitions, memory, retrieval and evaluation rather than prompts alone. Paloren treats the company brain as the foundation: a governed knowledge layer that grounds every answer in approved content. On top of that layer sit agents for reporting, CRM automation, call analysis and content production, the same patterns first proven inside Louder before Paloren was formed. For developer teams the practical build list looks like this: define the tools an agent may touch, write the policies that bound its behaviour, build the retrieval path from your sources, and instrument everything so each decision can be traced. That is engineering work, and it rewards teams that approach agents with the same discipline they apply to any production system. Paloren supplies the architecture, the guardrails and the patterns so your engineers spend time on product logic instead of rediscovering known failure modes.
- Orchestration, tools, memory and retrieval
- Company brain as the grounding layer
- Tracing and evaluation built in from day one
02 / 09AI Agents Developers Can Ship: Strategy, Builds and Support From Paloren
Why should developers bring Paloren into agent projects?
Paloren provides AI strategy, implementation, automation and training for companies worldwide, and agent work sits at the centre of that offer. The practice was co-founded by Aaron Agius and Alex Agius. Aaron founded Louder, a growth agency, and spent 15 years building marketing, data and growth systems; he is the author of Faster, Smarter, Louder (2019) and has published with Entrepreneur, Salesforce, HubSpot and the Forbes Agency Council. That background matters to developers because agents fail for business reasons far more often than technical ones: unclear ownership, unmapped processes, missing guardrails. The people behind Paloren spent two decades inside businesses such as IBM, Ford, LG, Unilever, Jaguar and Chelsea FC, so guidance arrives from operators who have carried delivery risk themselves. Engagements are built to complement internal engineers rather than replace them. Paloren defines architecture, evaluation and governance, then builds alongside your team and hands over documented systems your developers can run. The result is fewer wasted sprints, agent behaviour that survives contact with real data, and a pattern library your engineers reuse on every project that follows.
- Co-founded by Aaron Agius and Alex Agius
- Operator experience from IBM, Ford, LG, Unilever, Jaguar and Chelsea FC
- Built to complement internal engineering teams
AI agent project ranges for developer teams
Planning ranges only; final scope is confirmed after the readiness assessment.
| Project | Scope | Range and timeline |
|---|---|---|
| AI agents | Multi-step agents that reason over tools and act in systems | USD 40k-90k over 6-10 wks |
| Company brain | Central knowledge layer that grounds agent answers | USD 60k-150k over 8-12 wks |
| Workflow automation and integrations | Pipelines that connect agents to everyday tools | USD 15k-60k over 3-8 wks |
| Chatbot | Conversational front end for support and sales | USD 20k-50k over 4-8 wks |
| Voice agent or receptionist | Phone and voice handling with escalation to people | USD 25k-60k over 4-8 wks |
| CRM implementation with AI | Pipeline, contact and activity automation | USD 20k-80k over 4-10 wks |
| Custom apps | Purpose-built interfaces around agent workflows | From USD 40k |
Source: Fact bank
What shapes scope in an agent ai developers engagement
Scope drivers Paloren reviews during the AI readiness assessment.
| Factor | What it covers | Effect on build |
|---|---|---|
| Systems to connect | CRMs, data warehouses, ticketing, telephony | Each integration adds mapping, testing and monitoring work |
| Evaluation depth | Test sets, scoring, regression checks | Deeper evaluation lengthens build but reduces live incidents |
| Autonomy level | Assist, draft, or act without approval | Higher autonomy requires stronger guardrails and audit trails |
| Knowledge readiness | Clean, permissioned source content | Sparse content extends the company brain phase |
| Voice requirements | Real-time speech, telephony, interruption handling | Voice adds latency engineering and telephony integration |
| Team enablement | Developer and operator training | Training adds time up front and cuts support load later |
Source: Fact bank
Who is behind Paloren
Paloren is co-founded by Aaron Agius and Alex Agius. Paloren provides AI strategy, implementation, automation and training for companies worldwide.
03 / 09AI Agents Developers Can Ship: Strategy, Builds and Support From Paloren
How does Paloren approach agent architecture with engineering teams?
Architecture starts with the company brain, because an agent is only as trustworthy as the content it can see. Paloren maps your sources, applies permissions and builds a retrieval layer that returns governed answers. Tool design comes next. Every action an agent may take, from updating a CRM record to drafting a report, is defined as a scoped tool with clear inputs, outputs and failure states. Model choice is treated as a per-task decision rather than a default: classification, drafting, speech and reasoning are matched to the model that handles them well, and choices are documented so they can be revisited as models change. Evaluation is designed before build, with test sets drawn from real cases and regression checks that run on every change. Human handoff paths are explicit, so an agent that hits low confidence escalates to a person with full context. Observability closes the loop: traces, cost tracking and behaviour dashboards give developers the same view of agents they expect from any service. The sequence is deliberate, and it is why Paloren engagements begin with an AI readiness assessment before any agent code is written.
- Company brain grounds every agent answer
- Scoped tools with defined failure states
- Evaluation and observability designed before build
04 / 09AI Agents Developers Can Ship: Strategy, Builds and Support From Paloren
Which agent projects suit developer teams first?
The strongest first builds sit where volume, rules and data already exist. Support and sales chatbots handle repetitive conversations and hand complex cases to people, and they give developers a contained surface for learning evaluation and guardrail work. Call analysis and voice agents suit teams drowning in phone traffic: transcription, summarisation and routing run without changing your telephony stack. CRM automation with AI appeals to engineering groups that own revenue systems, because enrichment, activity capture and next-step suggestions remove manual entry across whole pipelines. Reporting agents assemble data from scattered sources into briefings, the pattern Paloren refined while the work still lived inside Louder. Workflow automation and integrations connect agents to ticketing, documents and internal tools, often the fastest path to visible time savings. Custom apps from USD 40k wrap agent behaviour in purpose-built interfaces when off-the-shelf screens cannot express the workflow. Most teams sequence one contained agent, then expand. The table on this page lists scope and timeline ranges for each project type, and the readiness assessment shows which option carries the best ratio of effort to impact for your stack.
- Chatbots and call analysis as contained first builds
- CRM automation for teams that own revenue systems
- Custom apps from USD 40k for unique workflows
05 / 09AI Agents Developers Can Ship: Strategy, Builds and Support From Paloren
What does the Paloren delivery process look like for developers?
Delivery follows five stages, each with outputs your engineers can inspect. An AI readiness assessment, from USD 8k over 2-3 wks, maps data quality, system access, security constraints and team skills. AI strategy, USD 12k-25k over 3-4 wks, turns findings into a sequenced plan with guardrails and success measures agreed by engineering and leadership together. Architecture and design then fix the company brain structure, tool contracts, model assignments and the evaluation plan, written as documents your team reviews before build starts. Build runs in sprints, typically USD 40k-90k over 6-10 wks for agent work, with code in your repositories, environments in your cloud and demos at the end of every sprint. Launch covers monitoring, cost controls and rollback paths, followed by team AI training so developers and operators can run the system without outside help. Ongoing support starts from USD 2,500/mo for 10 hrs and covers tuning, model updates and new tool integrations. Every stage ends in a handover artifact, which means knowledge moves into your team continuously rather than arriving in a single handover at the end.
- Assessment and strategy before any code
- Sprints in your repositories and your cloud
- Handover artifacts at every stage
06 / 09AI Agents Developers Can Ship: Strategy, Builds and Support From Paloren
How much do AI agent development projects cost?
Paloren quotes ranges rather than fixed prices because scope varies with integrations, autonomy and evaluation depth. A first project generally lands between USD 25k-100k over 2-10 wks. Dedicated agent builds run USD 40k-90k over 6-10 wks, while workflow automation and integrations sit at USD 15k-60k over 3-8 wks. A company brain, the knowledge layer that grounds agent answers, is the largest single item at USD 60k-150k over 8-12 wks. Chatbots range USD 20k-50k over 4-8 wks and voice agents USD 25k-60k over 4-8 wks, with CRM implementation with AI at USD 20k-80k over 4-10 wks. Custom apps start from USD 40k. After launch, support from USD 2,500/mo for 10 hrs keeps agents tuned as models and data change. Developers planning budgets should treat the assessment as the cheapest way to narrow these ranges: for USD 8k and 2-3 weeks, it converts broad estimates into a scoped plan. The table below sets out the full range list so engineering and finance can model scenarios before any commitment is made.
- First projects USD 25k-100k over 2-10 wks
- Agent builds USD 40k-90k over 6-10 wks
- Support from USD 2,500/mo for 10 hrs
07 / 09AI Agents Developers Can Ship: Strategy, Builds and Support From Paloren
How do Paloren agents connect to existing developer stacks?
Integration is a core service line, not an afterthought. Workflow automation and integrations, scoped at USD 15k-60k over 3-8 wks, links agents to the systems your team already runs: CRMs, data warehouses, ticketing, telephony, document stores and internal APIs. Paloren builds these connections with the same patterns developers use elsewhere, including scoped credentials, idempotent writes, retry logic and dead letter handling, so agent actions behave predictably under failure. CRM implementation with AI deserves a specific mention because revenue systems carry the most sensitive write paths; the range there is USD 20k-80k over 4-10 wks and covers enrichment, activity capture and pipeline automation with full audit trails. Voice agents and receptionists, USD 25k-60k over 4-8 wks, plug into existing telephony rather than replacing it, with escalation to people preserved at every step. This orientation comes from history: Paloren's AI work began inside Louder with AI reporting, CRM automation, call analysis and content systems, all of which had to live inside production stacks from day one. Your developers receive the integration code, the credentials design and the monitoring views as part of handover.
- Scoped credentials, retries and audit trails as standard
- Voice agents plug into existing telephony
- Integration code handed to your developers
08 / 09AI Agents Developers Can Ship: Strategy, Builds and Support From Paloren
What governance do developers need around AI agents?
Agents that act, rather than only answer, need controls that match their reach. Paloren treats AI governance as a build component with the same weight as retrieval or tool design. Access is scoped so an agent holds only the permissions its tasks require, and every action writes an audit entry a developer can query. Confidence thresholds decide when an agent proceeds and when it escalates to a person, and those thresholds are configurable per action rather than global. Evaluation runs continuously: test sets from real cases catch regressions before they reach production, and behaviour dashboards surface drift early. Data handling rules define what content may ground answers, what may be logged and what must never leave a boundary. These controls are documented in a governance playbook delivered at handover, written so engineers can maintain them without specialist help. Teams that want a structured starting point often begin with the AI readiness assessment, from USD 8k over 2-3 wks, which reviews data, systems and risk posture and produces a findings report your security and engineering leads can act on immediately.
- Scoped permissions with queryable audit entries
- Per-action confidence thresholds and escalation
- Governance playbook maintained by your engineers
09 / 09AI Agents Developers Can Ship: Strategy, Builds and Support From Paloren
How does Paloren prepare developer teams to run agents?
Agents are software, and software needs owners. Team AI training is a Paloren service built for exactly that moment when a project ends and internal ownership begins. Sessions cover the architecture in depth, the evaluation suite, how to read traces and dashboards, how to adjust confidence thresholds safely and how to add a new tool without breaking existing behaviour. Operators receive parallel training focused on daily use, escalation handling and reporting, so business teams and engineering teams share one mental model of what the system does. Documentation follows the same principle: runbooks, decision records and architecture notes are written for your people, not for a handover meeting. This emphasis traces back to the founders. Aaron Agius spent 15 years building marketing, data and growth systems at Louder and wrote Faster, Smarter, Louder (2019), and Alex Agius co-leads delivery with the same operator mindset. Training is delivered remotely to teams worldwide, and it is included in the sequencing of every build so enablement never becomes an afterthought. The goal is simple: your developers finish the engagement able to extend the system without depending on Paloren for every change.
- Architecture, evaluation and trace training for developers
- Operator training for daily use and escalation
- Runbooks and decision records written for your team
What you take forward
What you get
Agent architecture documents and decision records
Working agents deployed inside your stack and cloud
Evaluation suite with regression tests for every agent
Governance playbook covering access, audit and escalation
Training sessions for developers and operators
Ongoing support from USD 2,500/mo for 10 hrs
- 01
AI readiness assessment
A short engagement, from USD 8k over 2-3 wks, that maps data, systems, security posture and skills so agent plans rest on evidence rather than assumptions.
- 02
AI strategy
USD 12k-25k over 3-4 wks to set agent priorities, guardrails and sequencing, agreed jointly by engineering and leadership before build begins.
- 03
Architecture and design
Fix the company brain structure, tool contracts, model assignments and evaluation plan, written as documents your engineers review before code starts.
- 04
Build and integrate
Agents are built in sprints inside your repositories and cloud, wired into your stack and tested against agreed evaluation sets, typically USD 40k-90k over 6-10 wks.
- 05
Launch, train and support
Monitoring goes live, teams are trained on operation and escalation, and ongoing help starts from USD 2,500/mo for 10 hrs.
| Stage | What it changes |
|---|---|
| AI readiness assessment | A short engagement, from USD 8k over 2-3 wks, that maps data, systems, security posture and skills so agent plans rest on evidence rather than assumptions. |
| AI strategy | USD 12k-25k over 3-4 wks to set agent priorities, guardrails and sequencing, agreed jointly by engineering and leadership before build begins. |
| Architecture and design | Fix the company brain structure, tool contracts, model assignments and evaluation plan, written as documents your engineers review before code starts. |
| Build and integrate | Agents are built in sprints inside your repositories and cloud, wired into your stack and tested against agreed evaluation sets, typically USD 40k-90k over 6-10 wks. |
| Launch, train and support | Monitoring goes live, teams are trained on operation and escalation, and ongoing help starts from USD 2,500/mo for 10 hrs. |
What should your first agent do?
Send a short outline of the systems you use and the workflow you want agents to handle. Paloren replies with an assessment plan, indicative ranges and a sequencing view for your engineering team.
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 an AI agent in developer terms?
An agent is software that pursues a goal across multiple steps: it plans, calls tools such as APIs and databases, checks its own output and escalates to a person when confidence drops. Unlike a single model call, an agent holds state, follows policies and acts inside systems. Developers build the orchestration, retrieval, evaluation and guardrails that make that behaviour safe and repeatable in production.
We have strong engineers. Why involve Paloren?
Strong engineers still benefit from patterns that already work. Paloren brings tested architecture for company brains, tool design, evaluation and governance, refined since the AI work began inside Louder with reporting, CRM automation and call analysis. Your team keeps ownership of code and infrastructure while Paloren removes guesswork from model choice, guardrails and sequencing, which shortens the path from plan to production.
Which models and tools do Paloren agents use?
Model choice is made per task and documented, because classification, drafting, speech and reasoning each favour different options. Paloren avoids lock-in: agents are built against abstractions so models can be swapped as the landscape changes, and evaluation results drive any switch. Frameworks, vector stores and infrastructure are selected to fit your existing stack, and every choice is recorded in decision notes your developers keep.
Can agents run inside our existing stack and cloud?
Yes. Builds run in your repositories and your environments, and integrations connect agents to the CRMs, warehouses, ticketing and telephony you already operate. Scoped credentials, retry logic and audit trails are standard, and voice agents plug into current phone systems rather than replacing them. Handover includes the integration code and monitoring views, so your developers operate the whole system after launch.
What does a first AI agent project cost?
First projects generally fall between USD 25k-100k over 2-10 wks depending on scope. Dedicated agent builds run USD 40k-90k over 6-10 wks, and workflow automation sits at USD 15k-60k over 3-8 wks. The AI readiness assessment, from USD 8k over 2-3 wks, is the lowest cost way to convert these broad ranges into a scoped plan with firm numbers.
How quickly can developers see a working agent?
Assessment takes 2-3 weeks and strategy 3-4 weeks, so direction is set within roughly a month. Agent builds then run 6-10 weeks, with sprints producing working software your team can test from early on. Chatbots ship within 4-8 weeks and voice agents within 4-8 weeks. Every sprint ends in a demo, so progress is visible throughout rather than only at launch.
Does Paloren train developer teams to maintain agents?
Yes. Team AI training is a core service, covering architecture, the evaluation suite, traces, dashboards and safe threshold changes. Developers learn how to add tools without breaking existing behaviour, while operators train on daily use and escalation. Runbooks, decision records and architecture notes are written for your people, and ongoing support from USD 2,500/mo for 10 hrs is available when you want backup.
Where does Paloren work with developer teams?
Paloren serves businesses worldwide and delivers engagements remotely, so location does not limit participation. Country pages describe availability at a country level only. Assessments, strategy, builds and training all run across time zones with async documentation and scheduled sessions, which suits engineering teams already distributed. Aaron Agius ran systems this way at Louder for 15 years before co-founding Paloren.
What should your first agent do?